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@@ -182,3 +182,4 @@ parrot/lib/python3.10/site-packages/pillow.libs/libpng16-58efbb84.so.16.43.0 fil
 parrot/lib/libsqlite3.so.0.8.6 filter=lfs diff=lfs merge=lfs -text
 parrot/bin/sqlite3 filter=lfs diff=lfs merge=lfs -text
 parrot/lib/libsqlite3.so filter=lfs diff=lfs merge=lfs -text
+parrot/lib/libtcl8.6.so filter=lfs diff=lfs merge=lfs -text

parrot/lib/libtcl8.6.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25a7d41e52b5f49fb3c9cad8d669ce2c0c3d8372a746d7a44dd1427e9c1591c7
+size 1983416

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/checkpoint/base_engine.py

@@ -0,0 +1,41 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from abc import ABC, abstractmethod
+from typing import Iterable, Tuple
+
+import torch
+
+#from .huggingface_engine import HuggingFaceCheckpointEngine
+
+MEGATRON = 'megatron'
+HUGGINGFACE = 'huggingface'
+
+
+class CheckpointEngineBase(ABC):
+    """
+    Abstract interface for checkpoint engines to implement.
+
+    There is no ``__init__`` method here by design, since the creation of the checkpoint
+    engine will happen outside the policy/engine code. The tradeoff being made here is
+    that we will write different frontends for different checkpoint engines, but these
+    frontends can be tailored to the specific checkpoint engine/model source needs.
+    """
+
+    @abstractmethod
+    def parameters(self) -> Iterable[Tuple[str, torch.Tensor]]:
+        """
+        This method should create a generator of tuples of the form (name, parameter) for
+        all parameters in the model. The name should be the fully qualified name of the
+        parameter, and the parameter should be a torch.Tensor.
+
+        The expected use of a checkpoint engine is the following:
+        ```python
+        for name, parameter in checkpoint_engine.parameters():
+            container_map.map_param(name, parameter)
+        ```
+        For a concrete use example, see ``InferenceV2Policy``.
+        """
+        ...

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/checkpoint/huggingface_engine.py

@@ -0,0 +1,124 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import os
+import json
+import torch
+from .base_engine import CheckpointEngineBase
+from typing import Iterable, Tuple
+from functools import partial
+
+from ..logging import inference_logger
+
+
+class HuggingFaceCheckpointEngine(CheckpointEngineBase):
+
+    def __init__(self, model_name_or_path: str, auth_token: str = None) -> None:
+        super().__init__()
+        from transformers import AutoConfig, GenerationConfig
+
+        self.model_name_or_path = model_name_or_path
+        self.auth_token = auth_token
+        self.model_config = AutoConfig.from_pretrained(self.model_name_or_path)
+        # Define this property here so we can use it in the model implementation
+        if not hasattr(self.model_config, "max_seq_length"):
+            if hasattr(self.model_config, "max_position_embeddings"):
+                self.model_config.max_seq_length = self.model_config.max_position_embeddings
+            else:
+                generation_config = GenerationConfig.from_pretrained(self.model_name_or_path)
+                self.model_config.max_seq_length = generation_config.max_length
+        self._local_checkpoint_dir = None
+        self._all_ckpt_paths = self._fetch_checkpoint_files()
+
+    def _fetch_checkpoint_files(self):
+        """
+        Fetch the checkpoint files from the HuggingFace Hub.
+        """
+        # TODO(jeff): for models like llama-2 the user will have to provide an auth `token`,
+        # currently coming from the ckpt engine init but maybe a catch all kwargs for other
+        # snapshot download parameters would be more flexible.
+
+        from huggingface_hub import snapshot_download, list_repo_tree
+
+        def model_has_safetensors(model_name_or_path: str) -> bool:
+            if os.path.isdir(model_name_or_path):
+                file_list = os.listdir(model_name_or_path)
+            else:
+                file_list = [rf.path for rf in list_repo_tree(model_name_or_path)]
+            for f in file_list:
+                if f.endswith(".safetensors"):
+                    return True
+            return False
+
+        if os.path.isdir(self.model_name_or_path):
+            self._local_checkpoint_dir = self.model_name_or_path
+        else:
+            # We need to download the checkpoint files from HF
+            if model_has_safetensors(self.model_name_or_path):
+                # Prioritize downloading safetensors if they are available
+                allow_patterns = ["*.safetensors", "*.json"]
+            else:
+                # Fallback to bin files when safetensors are not present
+                allow_patterns = ["*.bin", "*.json", "*.pt"]
+            self._local_checkpoint_dir = snapshot_download(self.model_name_or_path,
+                                                           allow_patterns=allow_patterns,
+                                                           revision=None,
+                                                           token=self.auth_token)
+
+        assert os.path.isdir(
+            self._local_checkpoint_dir
+        ), f"Checkpoint dir {self._local_checkpoint_dir} is not a directory, cannot load checkpoint."
+
+        # Set the appropriate file names based on whether we have safetensors or not
+        if model_has_safetensors(self._local_checkpoint_dir):
+            from safetensors.torch import load_file
+            model_param_json_fname = "model.safetensors.index.json"
+            model_file_fname = "model.safetensors"
+            self._checkpoint_load_fn = load_file
+        else:
+            model_param_json_fname = "pytorch_model.bin.index.json"
+            model_file_fname = "pytorch_model.bin"
+            self._checkpoint_load_fn = partial(torch.load, map_location="cpu")
+
+        model_param_json = os.path.join(self._local_checkpoint_dir, model_param_json_fname)
+
+        if not os.path.isfile(model_param_json):
+            # We don't need any json as all such HF models will have pytorch_model.bin
+            all_checkpoint_files = [os.path.join(self._local_checkpoint_dir, model_file_fname)]
+        else:
+            param_map = json.load(open(model_param_json, "r"))
+
+            # weight_map -> { "lm_head.weight": "pytorch_model-00002-of-00002.bin", ... }
+            weight_map = param_map["weight_map"]
+
+            # unique set of all checkpoint files
+            all_checkpoint_files = set(weight_map.values())
+
+            # get absolute path of all unique checkpoint files
+            all_checkpoint_files = [os.path.join(self._local_checkpoint_dir, f) for f in all_checkpoint_files]
+
+        return all_checkpoint_files
+
+    def parameters(self) -> Iterable[Tuple[str, torch.Tensor]]:
+        """
+        Generator of model parameters (satisfies the CheckpointEngineBase interface).
+        """
+        for checkpoint in self._all_ckpt_paths:
+            inference_logger().info(f"Loading checkpoint: {checkpoint}")
+            checkpoint_sd = self._checkpoint_load_fn(checkpoint)
+            param_keys = list(checkpoint_sd.keys())
+            for param_name in param_keys:
+                param = checkpoint_sd[param_name]
+                yield param_name, param
+
+            del checkpoint_sd
+
+
+if __name__ == "__main__":
+    # To test, add your auth_token here and run `python huggingface_engine.py`
+    engine = HuggingFaceCheckpointEngine(model_name_or_path="meta-llama/Llama-2-7b-hf",
+                                         auth_token="hf_xxxxxxxxxxxxxxxxx")
+    for name, param in engine.parameters():
+        print(name, param.shape)

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/checkpoint/in_memory_engine.py

@@ -0,0 +1,40 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from typing import Iterable, Tuple
+import torch
+
+from .base_engine import CheckpointEngineBase
+
+
+class InMemoryModelEngine(CheckpointEngineBase):
+    """
+    This "checkpoint" engine uses the existing interface to enable loading parameters into an
+    inference model from a model already instantiated in memory. In general, this is not the
+    recommended way to use the inference engine, and should only be used when absolutely necessary.
+
+    The primary limitation of this approach is that the model must be fully instantiated in memory.
+    In a tensor parallel scenario, this means that the model is either replicated many times in host
+    memory. Currently, it is also recommended to only use this approach for models held in host memory.
+
+    In order to free the memory held by this copy of the model, we delete the model in the first call
+    to `parameters`, so it is not safe to make this call twice.
+    """
+
+    def __init__(self, model: torch.nn.Module) -> None:
+        """
+        Create virtual checkpoint engine for the provided module.
+
+        Args:
+            model (torch.nn.Module): Model to load parameters from.
+        """
+        super().__init__()
+        self.model = model
+
+    def parameters(self) -> Iterable[Tuple[str, torch.Tensor]]:
+        for name, parameter in self.model.named_parameters():
+            yield name, parameter
+
+        del self.model

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/falcon/model.py

@@ -0,0 +1,213 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from typing import Iterable, Optional, Tuple
+
+import torch
+
+import deepspeed.comm as dist
+
+from ...allocator import empty_from
+from ...inference_utils import ActivationType, DtypeEnum
+from .. import *
+from ...modules.configs import *
+from ...modules.interfaces import *
+from ...ragged import RaggedBatchWrapper
+
+from .container import FalconNonTransformerContainer, FalconTransformerContainer
+
+
+class FalconInferenceModel(DSTransformerModelBase):
+    """
+    Inference model implementation for ragged batching for Llama-2 models.
+    """
+
+    _non_transformer: Optional[FalconNonTransformerContainer]
+    """
+    Embed + unembed container. Specializing the type annotation.
+    """
+
+    _transformer: Optional[Iterable[FalconTransformerContainer]]
+    """
+    Per-layer transformer container. Specializing the type annotation.
+    """
+    """
+    Properties inherited from `DSInferenceModelBase`
+    """
+
+    @property
+    def max_sequence_length(self) -> int:
+        return self._config.max_seq_length
+
+    """
+    Properties inherited from `DSTransformerModelBase`
+    """
+
+    @property
+    def num_layers(self) -> int:
+        return self._config.num_hidden_layers
+
+    @property
+    def model_dim(self) -> int:
+        return self._config.hidden_size
+
+    @property
+    def vocab_size(self) -> int:
+        return self._config.vocab_size
+
+    @property
+    def head_size(self) -> int:
+        return self.model_dim // self.n_heads
+
+    @property
+    def n_heads(self) -> int:
+        return self._config.num_attention_heads
+
+    @property
+    def intermediate_dim(self) -> int:
+        return 4 * self._config.hidden_size
+
+    @property
+    def n_heads_kv(self) -> int:
+        return self._config.num_kv_heads if (self._config.new_decoder_architecture
+                                             or not self._config.multi_query) else 1
+
+    @property
+    def activation_dtype(self) -> DtypeEnum:
+        if self._config.torch_dtype == torch.float16:
+            return DtypeEnum.fp16
+        elif self._config.torch_dtype == torch.bfloat16:
+            return DtypeEnum.bf16
+        else:
+            raise NotImplementedError("Only fp16 and bf16 are supported")
+
+    @property
+    def mlp_activation_fn(self) -> ActivationType:
+        return ActivationType.GELU
+
+    @property
+    def norm_type(self) -> NormTypeEnum:
+        return NormTypeEnum.LayerNorm
+
+    @property
+    def positional_embedding_type(self) -> PositionalEmbeddingType:
+        return PositionalEmbeddingType.rotate_half
+
+    @property
+    def positional_embedding_config(self) -> RotateHalfConfig:
+        """
+        The positional embedding configuration for the model.
+        """
+        return RotateHalfConfig()
+
+    """
+    Forward implementations
+    """
+
+    def _forward_embed(self, ragged_batch: RaggedBatchWrapper) -> torch.Tensor:
+        """
+        Performs the embedding lookup prior to running the transformer of the model.
+
+        Arguments:
+            ragged_batch (RaggedBatchWrapper): The batch to embed.
+
+        Returns:
+            torch.Tensor: The embedded batch.
+        """
+        embed = self.embed(ragged_batch, self._non_transformer.word_emb)
+
+        if embed.shape[-1] != self.model_dim:
+            raise ValueError(f"Embedding output shape {embed.shape} does not match model_dim {self.model_dim}")
+
+        return embed
+
+    def _forward_transformer_layer(self, layer_idx: int, residual: torch.Tensor, hidden_states: torch.Tensor,
+                                   ragged_batch_info: RaggedBatchWrapper) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Executes one (slightly offset) layer of the transformer. This implementation does a peak-ahead
+        optimization to fuse the layer norm of the next layer into the current layer.
+
+        Arguments:
+            layer_idx (int): The index of the layer to execute.
+            residual (torch.Tensor): The residual tensor from the previous layer.
+            hidden_states (torch.Tensor): The hidden states from the previous layer. This is the
+                hidden states after pre normalization.
+            ragged_batch_info (RaggedBatchWrapper): The batch metadata.
+        """
+        assert self.config.parallel_attn, "Only parallel attention implementation is supported"
+
+        cur_params = self._transformer[layer_idx]
+        kv_cache = self.state_manager.get_cache(layer_idx)
+
+        attn_ln_out = hidden_states
+        attn_hidden_state = self.qkv(attn_ln_out, cur_params.qkv_w, b=None)
+        attn_hidden_state = self.attn(attn_hidden_state, kv_cache, ragged_batch_info)
+        attention_output = self.attn_out(attn_hidden_state, cur_params.attn_out_w, b=None)
+
+        if self.config.new_decoder_architecture:
+            residual, mlp_ln_out = self.norm(residual,
+                                             None,
+                                             gamma=cur_params.ln_mlp_gamma,
+                                             beta=cur_params.ln_mlp_beta)
+        else:
+            mlp_ln_out = hidden_states
+
+        mlp_hidden_state = self.mlp_1(mlp_ln_out, cur_params.mlp_1_w, b=None)
+        mlp_output = self.mlp_2(mlp_hidden_state, cur_params.mlp_2_w, b=None)
+
+        mlp_output.add_(attention_output)
+
+        if self.tp_size > 1:
+            dist.all_reduce(mlp_output, group=self._base_mp_group)
+
+        if layer_idx != self.num_layers - 1:
+            next_params = self._transformer[layer_idx + 1]
+            residual, mlp_output = self.norm(residual,
+                                             mlp_output,
+                                             next_params.ln_attn_gamma,
+                                             beta=next_params.ln_attn_beta)
+        else:
+            # On last layer, we just need to perform the residual add. Adding into the residual
+            # here is safe.
+            residual.add_(mlp_output)
+
+        return residual, mlp_output
+
+    def _forward_unembed(self, hidden_states: torch.Tensor, ragged_batch_info: RaggedBatchWrapper) -> torch.Tensor:
+        """
+        Performs unembedding of the hidden states to logits. This will only sample the final
+        token of each sequence.
+        """
+        logits = self.unembed(hidden_states,
+                              self._non_transformer.word_unembed,
+                              ragged_batch_info,
+                              gamma=self._non_transformer.final_norm_gamma,
+                              beta=self._non_transformer.final_norm_beta)
+
+        if self.tp_size > 1:
+            comm_buffer = empty_from(self._comm_logits, (self.tp_size, logits.shape[0], logits.shape[1]))
+            full_logits = empty_from(self._return_logits, (logits.shape[0], self.vocab_size))
+
+            dist.all_gather_into_tensor(comm_buffer, logits, group=self._base_mp_group)
+
+            full_logits.copy_(comm_buffer.permute(1, 0, 2).reshape(logits.shape[0], self.vocab_size))
+
+            return full_logits
+        else:
+            return logits
+
+    def forward(self, wrapped_batch: RaggedBatchWrapper) -> torch.Tensor:
+        residual = self._forward_embed(wrapped_batch)
+
+        residual, hidden_states = self.norm(residual,
+                                            None,
+                                            gamma=self._transformer[0].ln_attn_gamma,
+                                            beta=self._transformer[0].ln_attn_beta)
+
+        for layer_idx in range(self.num_layers):
+            residual, hidden_states = self._forward_transformer_layer(layer_idx, residual, hidden_states,
+                                                                      wrapped_batch)
+
+        return self._forward_unembed(residual, wrapped_batch)

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/falcon/policy.py

@@ -0,0 +1,33 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from typing import Any
+
+from ...config_v2 import RaggedInferenceEngineConfig
+from ..inference_policy_base import ContainerMap, InferenceV2Policy
+from .container import FalconNonTransformerContainer, FalconTransformerContainer
+from .container import FalconNewArchTransformerContainer
+from .model import FalconInferenceModel
+
+
+class FalconPolicy(InferenceV2Policy):
+
+    def instantiate_model(self, engine_config: RaggedInferenceEngineConfig, mp_group: Any) -> FalconInferenceModel:
+        return FalconInferenceModel(config=self._model_config, engine_config=engine_config, base_mp_group=mp_group)
+
+    def build_container_map(self) -> ContainerMap:
+        map = ContainerMap()
+
+        trans_container_cls = FalconNewArchTransformerContainer if self._model_config.new_decoder_architecture else FalconTransformerContainer
+        transformer_containers = [trans_container_cls(self.model) for _ in range(self.model.num_layers)]
+
+        map.set_transformer_params(['transformer.h'], transformer_containers)
+
+        map.set_non_transformer_params(FalconNonTransformerContainer(self.model))
+
+        map.set_unmapped_params(
+            [f'model.layers.{i}.self_attn.rotary_emb.inv_freq' for i in range(self.model.num_layers)])
+
+        return map

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/mixtral/__init__.py

@@ -0,0 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from .policy import MixtralPolicy

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/mixtral/model.py

@@ -0,0 +1,261 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from typing import Iterable, Optional, Tuple
+
+import torch
+
+import deepspeed.comm as dist
+
+from ...allocator import empty_from
+from ...config_v2 import RaggedInferenceEngineConfig
+from ...inference_utils import ActivationType, DtypeEnum
+from ...model_implementations import *
+from ...modules.configs import *
+from ...modules.interfaces import *
+from ...ragged import RaggedBatchWrapper
+from ..inference_model_base import (
+    DSModelImplementationConfig,
+    MPType,
+)
+
+from .container import MixtralNonTransformerContainer, MixtralTransformerContainer
+
+
+class MixtralInferenceModel(DSMoETransformerModelBase):
+    """
+    Inference model implementation for Mixtral models.
+    """
+
+    _non_transformer: Optional[MixtralNonTransformerContainer]
+    """
+    Embed + unembed container. Specializing the type annotation.
+    """
+
+    _transformer: Optional[Iterable[MixtralTransformerContainer]]
+    """
+    Per-layer transformer container. Specializing the type annotation.
+    """
+    """
+    Properties ineherited from `DSInferenceModelBase`
+    """
+
+    @property
+    def max_sequence_length(self) -> int:
+        return self._config.max_position_embeddings
+
+    """
+    Properties ineherited from `DSTransformerModelBase`
+    """
+
+    @property
+    def num_layers(self) -> int:
+        return self._config.num_hidden_layers
+
+    @property
+    def model_dim(self) -> int:
+        return self._config.hidden_size
+
+    @property
+    def vocab_size(self) -> int:
+        return self._config.vocab_size
+
+    @property
+    def head_size(self) -> int:
+        return self.model_dim // self.n_heads
+
+    @property
+    def n_heads(self) -> int:
+        return self._config.num_attention_heads
+
+    @property
+    def intermediate_dim(self) -> int:
+        return self._config.intermediate_size
+
+    @property
+    def n_heads_kv(self) -> int:
+        return self._config.num_key_value_heads
+
+    @property
+    def activation_dtype(self) -> DtypeEnum:
+        if self._config.torch_dtype == torch.float16:
+            return DtypeEnum.fp16
+        elif self._config.torch_dtype == torch.bfloat16:
+            return DtypeEnum.bf16
+        else:
+            raise NotImplementedError("Only fp16 and bf16 are supported")
+
+    @property
+    def mlp_activation_fn(self) -> ActivationType:
+        activation = self._config.hidden_act.lower()
+        if activation == "gelu":
+            return ActivationType.GEGLU
+        elif activation == "relu":
+            return ActivationType.ReGLU
+        elif activation == "gegelu":
+            return ActivationType.GEGLU
+        elif activation == "silu":
+            return ActivationType.SiGLU
+        else:
+            raise NotImplementedError(f"Activation {activation} not supported")
+
+    @property
+    def norm_type(self) -> NormTypeEnum:
+        return NormTypeEnum.RMSNorm
+
+    @property
+    def positional_embedding_type(self) -> PositionalEmbeddingType:
+        return PositionalEmbeddingType.rotate_half
+
+    @property
+    def positional_embedding_config(self) -> Optional[RotateHalfConfig]:
+        """
+        The positional embedding configuration for the model.
+        """
+        return RotateHalfConfig(theta_base=self._config.rope_theta)
+
+    """
+    Inherited from `DSMoETransformerModelBase`
+    """
+
+    @property
+    def n_experts(self) -> int:
+        return self._config.num_local_experts
+
+    @property
+    def n_top_k(self) -> int:
+        return self._config.num_experts_per_tok
+
+    @property
+    def normalize_expert_scores(self) -> bool:
+        return True
+
+    """
+    Model implementation
+    """
+
+    def __init__(self, config: DSModelImplementationConfig, engine_config: RaggedInferenceEngineConfig,
+                 base_mp_group: MPType) -> None:
+        """
+        Base implementation for initialization. By default, this will initialize
+        the traditional components of a transformer model:
+            - Embedding
+            - QKV projection
+            - Self attention
+            - Attention output projection
+            - Feed forward network
+            - Normalization
+            - Unembedding
+
+        Arguments:
+            config (DSModelImplementationConfig): Model-specific configuration. No assumptions
+                should be made about this config that are not closely tied to the specific
+                model implementation.
+            engine_config (RaggedInferenceEngineConfig): Engine configuration.
+            base_mp_group (MPType): Base communication group for Tensor-parallel inference.
+        """
+        super().__init__(config, engine_config, base_mp_group)
+
+        self.make_norm_layer()
+        self.make_qkv_layer()
+        self.make_attn_layer()
+        self.make_attn_out_layer()
+        self.make_moe_layer()
+        self.make_embedding_layer()
+        self.make_unembedding_layer()
+        self._kv_cache_config = None
+
+    def _forward_embed(self, ragged_batch: RaggedBatchWrapper) -> torch.Tensor:
+        """
+        Performs the embedding lookup prior to running the transformer of the model.
+
+        Arguments:
+            ragged_batch (RaggedBatchWrapper): The batch to embed.
+
+        Returns:
+            torch.Tensor: The embedded batch.
+        """
+        embed = self.embed(ragged_batch, self._non_transformer.word_emb)
+
+        if embed.shape[-1] != self.model_dim:
+            raise ValueError(f"Embedding output shape {embed.shape} does not match model_dim {self.model_dim}")
+
+        return embed
+
+    def _forward_transformer(self, layer_idx: int, residual: torch.Tensor, hidden_states: torch.Tensor,
+                             ragged_batch_info: RaggedBatchWrapper) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Executes one (slightly offset) layer of the transformer. This implementation does a peak-ahead
+        optimization to fuse the layer norm of the next layer into the current layer.
+
+        Arguments:
+            layer_idx (int): The index of the layer to execute.
+            residual (torch.Tensor): The residual tensor from the previous layer.
+            hidden_states (torch.Tensor): The hidden states from the previous layer. This is the
+                hidden states after pre normalization.
+            ragged_batch_info (RaggedBatchWrapper): The batch metadata.
+        """
+        # TODO(cmikeh2): Distribute ragged_batch_info to all modules
+
+        cur_params = self._transformer[layer_idx]
+        kv_cache = self.state_manager.get_cache(layer_idx)
+
+        hidden_states = self.qkv(hidden_states, cur_params.qkv_w)
+        hidden_states = self.attn(hidden_states, kv_cache, ragged_batch_info)
+        hidden_states = self.attn_out(hidden_states, cur_params.attn_out_w)
+
+        if self.tp_size > 1:
+            dist.all_reduce(hidden_states, group=self._base_mp_group)
+
+        residual, hidden_states = self.norm(residual, hidden_states, cur_params.mlp_norm_gamma)
+
+        hidden_states = self.moe(hidden_states, ragged_batch_info, cur_params.moe_gate, cur_params.moe_mlp_1,
+                                 cur_params.moe_mlp_2)
+
+        if self.tp_size > 1:
+            dist.all_reduce(hidden_states, group=self._base_mp_group)
+
+        if layer_idx != self.num_layers - 1:
+            next_params = self._transformer[layer_idx + 1]
+            residual, hidden_states = self.norm(residual, hidden_states, next_params.attn_norm_gamma)
+        else:
+            # On last layer, we just need to perform the residual add. Adding into the residual
+            # here is safe.
+            residual.add_(hidden_states)
+
+        return residual, hidden_states
+
+    def _forward_unembed(self, hidden_states: torch.Tensor, ragged_batch_info: RaggedBatchWrapper) -> torch.Tensor:
+        """
+        Performs unembedding of the hidden states to logits. This will only sample the final
+        token of each sequence.
+        """
+        logits = self.unembed(hidden_states,
+                              self._non_transformer.word_unembed,
+                              ragged_batch_info,
+                              gamma=self._non_transformer.final_norm)
+
+        if self.tp_size > 1:
+            comm_buffer = empty_from(self._comm_logits, (self.tp_size, logits.shape[0], logits.shape[1]))
+            full_logits = empty_from(self._return_logits, (logits.shape[0], self.vocab_size))
+
+            dist.all_gather_into_tensor(comm_buffer, logits, group=self._base_mp_group)
+
+            full_logits.copy_(comm_buffer.permute(1, 0, 2).reshape(logits.shape[0], self.vocab_size))
+
+            return full_logits
+        else:
+            return logits
+
+    def forward(self, wrapped_batch: RaggedBatchWrapper) -> torch.Tensor:
+
+        residual = self._forward_embed(wrapped_batch)
+
+        residual, hidden_states = self.norm(residual, None, self._transformer[0].attn_norm_gamma, beta=None)
+
+        for layer_idx in range(self.num_layers):
+            residual, hidden_states = self._forward_transformer(layer_idx, residual, hidden_states, wrapped_batch)
+
+        return self._forward_unembed(residual, wrapped_batch)

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/mixtral/policy.py

@@ -0,0 +1,31 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from typing import Any
+
+from ...config_v2 import RaggedInferenceEngineConfig
+from ..inference_policy_base import ContainerMap, InferenceV2Policy
+from .container import MixtralTransformerContainer, MixtralNonTransformerContainer
+from .model import MixtralInferenceModel
+
+
+class MixtralPolicy(InferenceV2Policy):
+
+    def instantiate_model(self, engine_config: RaggedInferenceEngineConfig, mp_group: Any) -> MixtralInferenceModel:
+        return MixtralInferenceModel(config=self._model_config, engine_config=engine_config, base_mp_group=mp_group)
+
+    def build_container_map(self) -> ContainerMap:
+
+        map = ContainerMap()
+
+        transformer_containers = [MixtralTransformerContainer(self.model) for _ in range(self.model.num_layers)]
+
+        map.set_transformer_params(['model.layers'], transformer_containers)
+
+        map.set_non_transformer_params(MixtralNonTransformerContainer(self.model))
+
+        map.set_unmapped_params([])
+
+        return map

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/opt/__init__.py

@@ -0,0 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from .policy import OPTPolicy

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/opt/container.py

@@ -0,0 +1,94 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# Create a container object to save model-specific tensors using the policy file above.
+
+from ..common_parameters import *
+from ..layer_container_base import LayerContainer
+'''
+ # HF OPT model looks like this:
+
+OPTForCausalLM(
+  (model): OPTModel(
+    (decoder): OPTDecoder(
+      (embed_tokens): Embedding(50272, 768, padding_idx=1)
+      (embed_positions): OPTLearnedPositionalEmbedding(2050, 768)
+      (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+      (layers): ModuleList(
+        (0-11): 12 x OPTDecoderLayer(
+          (self_attn): OPTAttention(
+            (k_proj): Linear(in_features=768, out_features=768, bias=True)
+            (v_proj): Linear(in_features=768, out_features=768, bias=True)
+            (q_proj): Linear(in_features=768, out_features=768, bias=True)
+            (out_proj): Linear(in_features=768, out_features=768, bias=True)
+          )
+          (activation_fn): ReLU()
+          (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+          (fc1): Linear(in_features=768, out_features=3072, bias=True)
+          (fc2): Linear(in_features=3072, out_features=768, bias=True)
+          (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
+        )
+      )
+    )
+  )
+  (lm_head): Linear(in_features=768, out_features=50272, bias=False)
+)
+
+'''
+
+
+class OPTTransformerContainer(LayerContainer):
+    """
+        Transformer layer container for the OPT model.
+    """
+    qkv_w: UnfusedQKVParameter
+    qkv_b: UnfusedQKVParameter
+    attn_out_w: AttentionOutputParameter
+    attn_out_b: AttentionOutputParameter
+    mlp_1_w: MLP1Parameter
+    mlp_1_b: MLP1Parameter
+    mlp_2_w: MLP2Parameter
+    mlp_2_b: MLP2Parameter
+    attn_norm_beta: NormParameter
+    attn_norm_gamma: NormParameter
+    mlp_norm_beta: NormParameter
+    mlp_norm_gamma: NormParameter
+
+    PARAM_MAPPING = {
+        "self_attn.q_proj.weight": "qkv_w.q_params",
+        "self_attn.q_proj.bias": "qkv_b.q_params",
+        "self_attn.k_proj.weight": "qkv_w.k_params",
+        "self_attn.k_proj.bias": "qkv_b.k_params",
+        "self_attn.v_proj.weight": "qkv_w.v_params",
+        "self_attn.v_proj.bias": "qkv_b.v_params",
+        "self_attn.out_proj.weight": "attn_out_w.params",
+        "self_attn.out_proj.bias": "attn_out_b.params",
+        "fc1.weight": "mlp_1_w.params",
+        "fc1.bias": "mlp_1_b.params",
+        "fc2.weight": "mlp_2_w.params",
+        "fc2.bias": "mlp_2_b.params",
+        "self_attn_layer_norm.weight": "attn_norm_gamma.params",
+        "self_attn_layer_norm.bias": "attn_norm_beta.params",
+        "final_layer_norm.weight": "mlp_norm_gamma.params",
+        "final_layer_norm.bias": "mlp_norm_beta.params",
+    }
+
+
+class OPTNonTransformerContainer(LayerContainer):
+    """
+        Non-Transformer layer container for the OPT model.
+    """
+    word_emb: EmbeddingParameter
+    word_emb_pos: EmbeddingParameter
+    word_unembed: UnembedParameter
+    final_norm_w: NormParameter
+    final_norm_b: NormParameter
+
+    PARAM_MAPPING = {
+        "*decoder.embed_tokens.weight": ["word_emb.params", "word_unembed.params"],
+        "*decoder.embed_positions.weight": "word_emb_pos.params",
+        "*decoder.final_layer_norm.weight": "final_norm_w.params",
+        "*decoder.final_layer_norm.bias": "final_norm_b.params",
+    }

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/opt/model.py

@@ -0,0 +1,197 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from typing import Iterable, Optional, Tuple
+
+import torch
+
+import deepspeed.comm as dist
+
+from ...allocator import empty_from
+from ...inference_utils import ActivationType, DtypeEnum
+from ...model_implementations import *
+from ...modules.configs import *
+from ...ragged import RaggedBatchWrapper
+from .container import OPTNonTransformerContainer, OPTTransformerContainer
+
+from ...modules.heuristics import instantiate_embed
+
+
+class OPTInferenceModel(DSTransformerModelBase):
+    """
+    Inference model implementation for ragged batching for OPT models.
+    """
+
+    _non_transformer: Optional[OPTNonTransformerContainer]
+    """
+    Embed + unembed container. Specializing the type annotation.
+    """
+
+    _transformer: Optional[Iterable[OPTTransformerContainer]]
+    """
+    Per-layer transformer container. Specializing the type annotation.
+    """
+    """
+    Properties ineherited from `DSInferenceModelBase`
+    """
+
+    @property
+    def max_sequence_length(self) -> int:
+        return self._config.max_seq_length
+
+    """
+    Properties ineherited from `DSTransformerModelBase`
+    """
+
+    @property
+    def num_layers(self) -> int:
+        return self._config.num_hidden_layers
+
+    @property
+    def model_dim(self) -> int:
+        return self._config.hidden_size
+
+    @property
+    def vocab_size(self) -> int:
+        return self._config.vocab_size
+
+    @property
+    def head_size(self) -> int:
+        return self.model_dim // self.n_heads
+
+    @property
+    def n_heads(self) -> int:
+        return self._config.num_attention_heads
+
+    @property
+    def intermediate_dim(self) -> int:
+        return self._config.ffn_dim
+
+    @property
+    def activation_dtype(self) -> DtypeEnum:
+        if self._config.torch_dtype == torch.float16:
+            return DtypeEnum.fp16
+        elif self._config.torch_dtype == torch.bfloat16:
+            return DtypeEnum.bf16
+        else:
+            raise NotImplementedError("Only fp16 and bf16 are supported")
+
+    @property
+    def mlp_activation_fn(self) -> ActivationType:
+        return ActivationType.RELU
+
+    @property
+    def norm_type(self) -> NormTypeEnum:
+        return NormTypeEnum.LayerNorm
+
+    @property
+    def positional_embedding_type(self) -> PositionalEmbeddingType:
+        return PositionalEmbeddingType.none
+
+    @property
+    def positional_embedding_config(self) -> Optional[RotateHalfConfig]:
+        return None
+
+    """
+    Overrides of ``DSTransformerModelBase`` methods
+    """
+
+    def make_embedding_layer(self) -> None:
+        """
+        Performs setup and creates embedding DSModule. Since OPT includes trained
+        positional embeddings, we will override the base model implementation.
+        """
+
+        embed_config = DSEmbeddingsConfig(max_tokens=self._engine_config.state_manager.max_ragged_batch_size,
+                                          residual_dtype=self.activation_dtype,
+                                          embedding_dim=self.model_dim,
+                                          positional_embedding=True,
+                                          positional_offset=2)
+
+        self.embed = instantiate_embed(embed_config, self._engine_config)
+
+    """
+    Forward implementations
+    """
+
+    def _forward_embed(self, ragged_batch: RaggedBatchWrapper) -> torch.Tensor:
+        embed = self.embed(ragged_batch, self._non_transformer.word_emb, self._non_transformer.word_emb_pos)
+        if embed.shape[-1] != self.model_dim:
+            raise ValueError(f"Embedding output shape {embed.shape} does not match model_dim {self.model_dim}")
+
+        return embed
+
+    def _forward_transformer_layer(self, layer_idx: int, residual: torch.Tensor, hidden_states: torch.Tensor,
+                                   ragged_batch_info: RaggedBatchWrapper) -> Tuple[torch.Tensor, torch.Tensor]:
+        # TODO(cmikeh2): Distribute ragged_batch_info to all modules
+
+        cur_params = self._transformer[layer_idx]
+        kv_cache = self.state_manager.get_cache(layer_idx)
+
+        hidden_states = self.qkv(hidden_states, cur_params.qkv_w, b=cur_params.qkv_b)
+        hidden_states = self.attn(hidden_states, kv_cache, ragged_batch_info)
+        hidden_states = self.attn_out(hidden_states, cur_params.attn_out_w, b=cur_params.attn_out_b)
+
+        if self.tp_size > 1:
+            dist.all_reduce(hidden_states, group=self._base_mp_group)
+
+        residual, hidden_states = self.norm(residual,
+                                            hidden_states,
+                                            cur_params.mlp_norm_gamma,
+                                            beta=cur_params.mlp_norm_beta)
+
+        # Should be configurable in the future
+        hidden_states = self.mlp_1(hidden_states, cur_params.mlp_1_w, b=cur_params.mlp_1_b)
+        hidden_states = self.mlp_2(hidden_states, cur_params.mlp_2_w, b=cur_params.mlp_2_b)
+
+        if self.tp_size > 1:
+            dist.all_reduce(hidden_states, group=self._base_mp_group)
+
+        if layer_idx != self.num_layers - 1:
+            next_params = self._transformer[layer_idx + 1]
+            residual, hidden_states = self.norm(residual,
+                                                hidden_states,
+                                                next_params.attn_norm_gamma,
+                                                beta=next_params.attn_norm_beta)
+        else:
+            # On last layer, we just need to perform the residual add. Adding into the residual
+            # here is safe.
+            residual.add_(hidden_states)
+
+        return residual, hidden_states
+
+    def _forward_unembed(self, hidden_states: torch.Tensor, ragged_batch_info: RaggedBatchWrapper) -> torch.Tensor:
+        logits = self.unembed(hidden_states,
+                              self._non_transformer.word_unembed,
+                              ragged_batch_info,
+                              gamma=self._non_transformer.final_norm_w,
+                              beta=self._non_transformer.final_norm_b)
+
+        if self.tp_size > 1:
+            comm_buffer = empty_from(self._comm_logits, (self.tp_size, logits.shape[0], logits.shape[1]))
+            full_logits = empty_from(self._return_logits, (logits.shape[0], self.vocab_size))
+
+            dist.all_gather_into_tensor(comm_buffer, logits, group=self._base_mp_group)
+
+            full_logits.copy_(comm_buffer.permute(1, 0, 2).reshape(logits.shape[0], self.vocab_size))
+
+            return full_logits
+        else:
+            return logits
+
+    def forward(self, wrapped_batch: RaggedBatchWrapper) -> torch.Tensor:
+
+        residual = self._forward_embed(wrapped_batch)
+
+        residual, hidden_states = self.norm(residual,
+                                            None,
+                                            self._transformer[0].attn_norm_gamma,
+                                            beta=self._transformer[0].attn_norm_beta)
+
+        for layer_idx in range(self.num_layers):
+            residual, hidden_states = self._forward_transformer_layer(layer_idx, residual, hidden_states,
+                                                                      wrapped_batch)
+
+        return self._forward_unembed(residual, wrapped_batch)

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/opt/policy.py

@@ -0,0 +1,30 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from typing import Any
+
+from ...config_v2 import RaggedInferenceEngineConfig
+from ..inference_policy_base import ContainerMap, InferenceV2Policy
+from .container import OPTNonTransformerContainer, OPTTransformerContainer
+from .model import OPTInferenceModel
+
+
+class OPTPolicy(InferenceV2Policy):
+
+    def instantiate_model(self, engine_config: RaggedInferenceEngineConfig, mp_group: Any) -> OPTInferenceModel:
+        return OPTInferenceModel(config=self._model_config, engine_config=engine_config, base_mp_group=mp_group)
+
+    def build_container_map(self) -> ContainerMap:
+        map = ContainerMap()
+
+        transformer_containers = [OPTTransformerContainer(self.model) for _ in range(self.model.num_layers)]
+
+        map.set_transformer_params(['model.decoder.layers', 'decoder.layers'], transformer_containers)
+
+        map.set_non_transformer_params(OPTNonTransformerContainer(self.model))
+
+        map.set_unmapped_params(['lm_head.weight'])
+
+        return map

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/qwen/__init__.py

@@ -0,0 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from .policy import QwenPolicy

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/qwen/container.py

@@ -0,0 +1,77 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# Create a container object to save model-specific tensors using the policy file above.
+
+from ..common_parameters import *
+from ..layer_container_base import LayerContainer
+'''
+ # HF Qwen model looks like this:
+
+QWenLMHeadModel(
+  (transformer): QWenModel(
+    (wte): Embedding(151936, 4096)
+    (drop): Dropout(p=0.0, inplace=False)
+    (rotary_emb): RotaryEmbedding()
+    (h): ModuleList(
+      (0-31): 32 x QWenBlock(
+        (ln_1): RMSNorm()
+        (attn): QWenAttention(
+          (c_attn): Linear(in_features=4096, out_features=12288, bias=True)
+          (c_proj): Linear(in_features=4096, out_features=4096, bias=False)
+          (attn_dropout): Dropout(p=0.0, inplace=False)
+        )
+        (ln_2): RMSNorm()
+        (mlp): QWenMLP(
+          (w1): Linear(in_features=4096, out_features=11008, bias=False)
+          (w2): Linear(in_features=4096, out_features=11008, bias=False)
+          (c_proj): Linear(in_features=11008, out_features=4096, bias=False)
+        )
+      )
+    )
+    (ln_f): RMSNorm()
+  )
+  (lm_head): Linear(in_features=4096, out_features=151936, bias=False)
+)
+'''
+
+
+class QwenTransformerContainer(LayerContainer):
+    """
+        Transformer layer container for the Qwen model.
+    """
+    qkv_w: FusedQKVParameter
+    qkv_b: FusedQKVParameter
+    attn_out_w: AttentionOutputParameter
+    mlp_1_w: GatedMLPParameter
+    mlp_2_w: MLP2Parameter
+    attn_norm_gamma: NormParameter
+    mlp_norm_gamma: NormParameter
+
+    PARAM_MAPPING = {
+        "attn.c_attn.weight": "qkv_w.params",
+        "attn.c_attn.bias": "qkv_b.params",
+        "attn.c_proj.weight": "attn_out_w.params",
+        "mlp.w1.weight": "mlp_1_w.up_params",
+        "mlp.w2.weight": "mlp_1_w.gate_params",
+        "mlp.c_proj.weight": "mlp_2_w.params",
+        "ln_1.weight": "attn_norm_gamma.params",
+        "ln_2.weight": "mlp_norm_gamma.params",
+    }
+
+
+class QwenNonTransformerContainer(LayerContainer):
+    """
+        Non-Transformer layer container for the Qwen model.
+    """
+    word_emb: EmbeddingParameter
+    word_unembed: UnembedParameter
+    final_norm: NormParameter
+
+    PARAM_MAPPING = {
+        "transformer.wte.weight": "word_emb.params",
+        "transformer.ln_f.weight": "final_norm.params",
+        "lm_head.weight": "word_unembed.params",
+    }

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/qwen/model.py

@@ -0,0 +1,223 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from typing import Iterable, Optional, Tuple
+
+import torch
+
+import deepspeed.comm as dist
+
+from ...allocator import empty_from
+from ...inference_utils import ActivationType, DtypeEnum
+from .. import *
+from ...modules.configs import *
+from ...modules.interfaces import *
+from ...modules import heuristics
+from ...ragged import RaggedBatchWrapper
+
+from .container import QwenNonTransformerContainer, QwenTransformerContainer
+
+
+class QwenInferenceModel(DSTransformerModelBase):
+    """
+    Inference model implementation for ragged batching for Llama-2 models.
+    """
+
+    _non_transformer: Optional[QwenNonTransformerContainer]
+    """
+    Embed + unembed container. Specializing the type annotation.
+    """
+
+    _transformer: Optional[Iterable[QwenTransformerContainer]]
+    """
+    Per-layer transformer container. Specializing the type annotation.
+    """
+    """
+    Properties ineherited from `DSInferenceModelBase`
+    """
+
+    @property
+    def max_sequence_length(self) -> int:
+        return self._config.max_seq_length
+
+    """
+    Properties ineherited from `DSTransformerModelBase`
+    """
+
+    @property
+    def num_layers(self) -> int:
+        return self._config.num_hidden_layers
+
+    @property
+    def model_dim(self) -> int:
+        return self._config.hidden_size
+
+    @property
+    def vocab_size(self) -> int:
+        return self._config.vocab_size
+
+    @property
+    def head_size(self) -> int:
+        return self.model_dim // self.n_heads
+
+    @property
+    def n_heads(self) -> int:
+        return self._config.num_attention_heads
+
+    @property
+    def intermediate_dim(self) -> int:
+        return self._config.intermediate_size // 2
+
+    @property
+    def n_heads_kv(self) -> int:
+        return self._config.hidden_size // self._config.kv_channels
+
+    @property
+    def activation_dtype(self) -> DtypeEnum:
+        autoset_precision = self._config.bf16 + self._config.fp16 == 0
+        if autoset_precision:
+            return DtypeEnum.fp16
+        if self._config.fp16:
+            return DtypeEnum.fp16
+        elif self._config.bf16:
+            # TODO(ZonePG): bf16 inference results may be different from huggingface bf16,
+            # because in rms_norm, Qwen still use float() instead of bf16
+            return DtypeEnum.bf16
+        else:
+            raise NotImplementedError("Only fp16 and bf16 are supported")
+
+    @property
+    def mlp_activation_fn(self) -> ActivationType:
+        return ActivationType.SiGLU
+
+    @property
+    def norm_type(self) -> NormTypeEnum:
+        return NormTypeEnum.RMSNorm
+
+    @property
+    def positional_embedding_type(self) -> PositionalEmbeddingType:
+        return PositionalEmbeddingType.rotate_half
+
+    @property
+    def positional_embedding_config(self) -> Optional[RotateHalfConfig]:
+        return RotateHalfConfig(theta_base=self._config.rotary_emb_base)
+
+    def make_norm_layer(self) -> None:
+        """
+        Instantiates the normalization layer for the model. This sets the `self.norm` attribute.
+
+        TODO(cmikeh2): In the future we'll distinguish between the different norm objects,
+        but for now we'll just use the same one for all of them.
+        """
+        norm_config = DSNormConfig(
+            max_tokens=self._engine_config.state_manager.max_ragged_batch_size,
+            type=self.norm_type,
+            channels=self.model_dim,
+            residual_dtype=self.activation_dtype,
+            input_dtype=self.activation_dtype,
+            output_dtype=self.activation_dtype,
+            eps=self._config.layer_norm_epsilon,
+        )
+
+        self.norm = heuristics.instantiate_pre_norm(norm_config, self._engine_config)
+
+    """
+    Forward implementations
+    """
+
+    def _forward_embed(self, ragged_batch: RaggedBatchWrapper) -> torch.Tensor:
+        """
+        Performs the embedding lookup prior to running the transformer of the model.
+
+        Arguments:
+            ragged_batch (RaggedBatchWrapper): The batch to embed.
+
+        Returns:
+            torch.Tensor: The embedded batch.
+        """
+        embed = self.embed(ragged_batch, self._non_transformer.word_emb)
+
+        if embed.shape[-1] != self.model_dim:
+            raise ValueError(f"Embedding output shape {embed.shape} does not match model_dim {self.model_dim}")
+
+        return embed
+
+    def _forward_transformer_layer(self, layer_idx: int, residual: torch.Tensor, hidden_states: torch.Tensor,
+                                   ragged_batch_info: RaggedBatchWrapper) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Executes one (slightly offset) layer of the transformer. This implementation does a peak-ahead
+        optimization to fuse the layer norm of the next layer into the current layer.
+
+        Arguments:
+            layer_idx (int): The index of the layer to execute.
+            residual (torch.Tensor): The residual tensor from the previous layer.
+            hidden_states (torch.Tensor): The hidden states from the previous layer. This is the
+                hidden states after pre normalization.
+            ragged_batch_info (RaggedBatchWrapper): The batch metadata.
+        """
+        # TODO(cmikeh2): Distribute ragged_batch_info to all modules
+
+        cur_params = self._transformer[layer_idx]
+        kv_cache = self.state_manager.get_cache(layer_idx)
+
+        hidden_states = self.qkv(hidden_states, cur_params.qkv_w, b=cur_params.qkv_b)
+        hidden_states = self.attn(hidden_states, kv_cache, ragged_batch_info)
+        hidden_states = self.attn_out(hidden_states, cur_params.attn_out_w, b=None)
+
+        if self.tp_size > 1:
+            dist.all_reduce(hidden_states, group=self._base_mp_group)
+
+        residual, hidden_states = self.norm(residual, hidden_states, cur_params.mlp_norm_gamma, beta=None)
+
+        # Should be configurable in the future
+        hidden_states = self.mlp_1(hidden_states, cur_params.mlp_1_w, b=None)
+        hidden_states = self.mlp_2(hidden_states, cur_params.mlp_2_w, b=None)
+
+        if self.tp_size > 1:
+            dist.all_reduce(hidden_states, group=self._base_mp_group)
+
+        if layer_idx != self.num_layers - 1:
+            next_params = self._transformer[layer_idx + 1]
+            residual, hidden_states = self.norm(residual, hidden_states, next_params.attn_norm_gamma, beta=None)
+        else:
+            # On last layer, we just need to perform the residual add. Adding into the residual
+            # here is safe.
+            residual.add_(hidden_states)
+
+        return residual, hidden_states
+
+    def _forward_unembed(self, hidden_states: torch.Tensor, ragged_batch_info: RaggedBatchWrapper) -> torch.Tensor:
+        """
+        Performs unembedding of the hidden states to logits. This will only sample the final
+        token of each sequence.
+        """
+        logits = self.unembed(hidden_states,
+                              self._non_transformer.word_unembed,
+                              ragged_batch_info,
+                              gamma=self._non_transformer.final_norm)
+
+        if self.tp_size > 1:
+            comm_buffer = empty_from(self._comm_logits, (self.tp_size, logits.shape[0], logits.shape[1]))
+            full_logits = empty_from(self._return_logits, (logits.shape[0], self.vocab_size))
+
+            dist.all_gather_into_tensor(comm_buffer, logits, group=self._base_mp_group)
+
+            full_logits.copy_(comm_buffer.permute(1, 0, 2).reshape(logits.shape[0], self.vocab_size))
+
+            return full_logits
+        else:
+            return logits
+
+    def forward(self, wrapped_batch: RaggedBatchWrapper) -> torch.Tensor:
+
+        residual = self._forward_embed(wrapped_batch)
+
+        residual, hidden_states = self.norm(residual, None, self._transformer[0].attn_norm_gamma, beta=None)
+
+        for layer_idx in range(self.num_layers):
+            residual, hidden_states = self._forward_transformer_layer(layer_idx, residual, hidden_states,
+                                                                      wrapped_batch)
+
+        return self._forward_unembed(residual, wrapped_batch)

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/qwen/policy.py

@@ -0,0 +1,30 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from typing import Any
+
+from ...config_v2 import RaggedInferenceEngineConfig
+from ..inference_policy_base import ContainerMap, InferenceV2Policy
+from .container import QwenNonTransformerContainer, QwenTransformerContainer
+from .model import QwenInferenceModel
+
+
+class QwenPolicy(InferenceV2Policy):
+
+    def instantiate_model(self, engine_config: RaggedInferenceEngineConfig, mp_group: Any) -> QwenInferenceModel:
+        return QwenInferenceModel(config=self._model_config, engine_config=engine_config, base_mp_group=mp_group)
+
+    def build_container_map(self) -> ContainerMap:
+        map = ContainerMap()
+
+        transformer_containers = [QwenTransformerContainer(self.model) for _ in range(self.model.num_layers)]
+
+        map.set_transformer_params(['transformer.h'], transformer_containers)
+
+        map.set_non_transformer_params(QwenNonTransformerContainer(self.model))
+
+        map.set_unmapped_params(['transformer.rotary_emb.inv_freq'])
+
+        return map

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/qwen_v2/__init__.py

@@ -0,0 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from .policy import Qwen2Policy

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/qwen_v2/container.py

@@ -0,0 +1,82 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# Create a container object to save model-specific tensors using the policy file above.
+
+from ..common_parameters import *
+from ..layer_container_base import LayerContainer
+'''
+ # HF Qwen2 model looks like this:
+
+Qwen2ForCausalLM(
+  (model): Qwen2Model(
+    (embed_tokens): Embedding(151936, 1024)
+    (layers): ModuleList(
+      (0-23): 24 x Qwen2DecoderLayer(
+        (self_attn): Qwen2SdpaAttention(
+          (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
+          (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
+          (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
+          (o_proj): Linear(in_features=1024, out_features=1024, bias=False)
+          (rotary_emb): Qwen2RotaryEmbedding()
+        )
+        (mlp): Qwen2MLP(
+          (gate_proj): Linear(in_features=1024, out_features=2816, bias=False)
+          (up_proj): Linear(in_features=1024, out_features=2816, bias=False)
+          (down_proj): Linear(in_features=2816, out_features=1024, bias=False)
+          (act_fn): SiLU()
+        )
+        (input_layernorm): Qwen2RMSNorm()
+        (post_attention_layernorm): Qwen2RMSNorm()
+      )
+    )
+    (norm): Qwen2RMSNorm()
+  )
+  (lm_head): Linear(in_features=1024, out_features=151936, bias=False)
+)
+'''
+
+
+class Qwen2TransformerContainer(LayerContainer):
+    """
+        Transformer layer container for the Qwen2 model.
+    """
+    qkv_w: UnfusedQKVParameter
+    qkv_b: UnfusedQKVParameter
+    attn_out_w: AttentionOutputParameter
+    mlp_1_w: GatedMLPParameter
+    mlp_2_w: MLP2Parameter
+    attn_norm_gamma: NormParameter
+    mlp_norm_gamma: NormParameter
+
+    PARAM_MAPPING = {
+        "self_attn.q_proj.weight": "qkv_w.q_params",
+        "self_attn.k_proj.weight": "qkv_w.k_params",
+        "self_attn.v_proj.weight": "qkv_w.v_params",
+        "self_attn.q_proj.bias": "qkv_b.q_params",
+        "self_attn.k_proj.bias": "qkv_b.k_params",
+        "self_attn.v_proj.bias": "qkv_b.v_params",
+        "self_attn.o_proj.weight": "attn_out_w.params",
+        "mlp.gate_proj.weight": "mlp_1_w.gate_params",
+        "mlp.up_proj.weight": "mlp_1_w.up_params",
+        "mlp.down_proj.weight": "mlp_2_w.params",
+        "input_layernorm.weight": "attn_norm_gamma.params",
+        "post_attention_layernorm.weight": "mlp_norm_gamma.params",
+    }
+
+
+class Qwen2NonTransformerContainer(LayerContainer):
+    """
+        Non-Transformer layer container for the Qwen2 model.
+    """
+    word_emb: EmbeddingParameter
+    word_unembed: UnembedParameter
+    final_norm: NormParameter
+
+    PARAM_MAPPING = {
+        "model.embed_tokens.weight": "word_emb.params",
+        "model.norm.weight": "final_norm.params",
+        "lm_head.weight": "word_unembed.params",
+    }

parrot/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/qwen_v2/model.py

@@ -0,0 +1,221 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from typing import Iterable, Optional, Tuple
+
+import torch
+
+import deepspeed.comm as dist
+
+from ...allocator import empty_from
+from ...inference_utils import ActivationType, DtypeEnum
+from .. import *
+from ...modules.configs import *
+from ...modules.interfaces import *
+from ...modules import heuristics
+from ...ragged import RaggedBatchWrapper
+
+from .container import Qwen2NonTransformerContainer, Qwen2TransformerContainer
+
+
+class Qwen2InferenceModel(DSTransformerModelBase):
+    """
+    Inference model implementation for ragged batching for Llama-2 models.
+    """
+
+    _non_transformer: Optional[Qwen2NonTransformerContainer]
+    """
+    Embed + unembed container. Specializing the type annotation.
+    """
+
+    _transformer: Optional[Iterable[Qwen2TransformerContainer]]
+    """
+    Per-layer transformer container. Specializing the type annotation.
+    """
+    """
+    Properties ineherited from `DSInferenceModelBase`
+    """
+
+    @property
+    def max_sequence_length(self) -> int:
+        return self._config.max_seq_length
+
+    """
+    Properties ineherited from `DSTransformerModelBase`
+    """
+
+    @property
+    def num_layers(self) -> int:
+        return self._config.num_hidden_layers
+
+    @property
+    def model_dim(self) -> int:
+        return self._config.hidden_size
+
+    @property
+    def vocab_size(self) -> int:
+        return self._config.vocab_size
+
+    @property
+    def head_size(self) -> int:
+        return self.model_dim // self.n_heads
+
+    @property
+    def n_heads(self) -> int:
+        return self._config.num_attention_heads
+
+    @property
+    def intermediate_dim(self) -> int:
+        return self._config.intermediate_size
+
+    @property
+    def n_heads_kv(self) -> int:
+        return self._config.num_key_value_heads
+
+    @property
+    def activation_dtype(self) -> DtypeEnum:
+        # TODO(ZonePG): bf16 inference results may be different from huggingface bf16,
+        # because in rms_norm, Qwen still use float() instead of bf16
+        # if self._config.torch_dtype == torch.float16:
+        #     return DtypeEnum.fp16
+        # elif self._config.torch_dtype == torch.bfloat16:
+        #     return DtypeEnum.bf16
+        # else:
+        #     raise NotImplementedError("Only fp16 and bf16 are supported")
+        return DtypeEnum.fp16
+
+    @property
+    def mlp_activation_fn(self) -> ActivationType:
+        return ActivationType.SiGLU
+
+    @property
+    def norm_type(self) -> NormTypeEnum:
+        return NormTypeEnum.RMSNorm
+
+    @property
+    def positional_embedding_type(self) -> PositionalEmbeddingType:
+        return PositionalEmbeddingType.rotate_half
+
+    @property
+    def positional_embedding_config(self) -> Optional[RotateHalfConfig]:
+        return RotateHalfConfig(theta_base=self._config.rope_theta)
+
+    def make_norm_layer(self) -> None:
+        """
+        Instantiates the normalization layer for the model. This sets the `self.norm` attribute.
+
+        TODO(cmikeh2): In the future we'll distinguish between the different norm objects,
+        but for now we'll just use the same one for all of them.
+        """
+        norm_config = DSNormConfig(
+            max_tokens=self._engine_config.state_manager.max_ragged_batch_size,
+            type=self.norm_type,
+            channels=self.model_dim,
+            residual_dtype=self.activation_dtype,
+            input_dtype=self.activation_dtype,
+            output_dtype=self.activation_dtype,
+            eps=self._config.rms_norm_eps,
+        )
+
+        self.norm = heuristics.instantiate_pre_norm(norm_config, self._engine_config)
+
+    """
+    Forward implementations
+    """
+
+    def _forward_embed(self, ragged_batch: RaggedBatchWrapper) -> torch.Tensor:
+        """
+        Performs the embedding lookup prior to running the transformer of the model.
+
+        Arguments:
+            ragged_batch (RaggedBatchWrapper): The batch to embed.
+
+        Returns:
+            torch.Tensor: The embedded batch.
+        """
+        embed = self.embed(ragged_batch, self._non_transformer.word_emb)
+
+        if embed.shape[-1] != self.model_dim:
+            raise ValueError(f"Embedding output shape {embed.shape} does not match model_dim {self.model_dim}")
+
+        return embed
+
+    def _forward_transformer_layer(self, layer_idx: int, residual: torch.Tensor, hidden_states: torch.Tensor,
+                                   ragged_batch_info: RaggedBatchWrapper) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Executes one (slightly offset) layer of the transformer. This implementation does a peak-ahead
+        optimization to fuse the layer norm of the next layer into the current layer.
+
+        Arguments:
+            layer_idx (int): The index of the layer to execute.
+            residual (torch.Tensor): The residual tensor from the previous layer.
+            hidden_states (torch.Tensor): The hidden states from the previous layer. This is the
+                hidden states after pre normalization.
+            ragged_batch_info (RaggedBatchWrapper): The batch metadata.
+        """
+        # TODO(cmikeh2): Distribute ragged_batch_info to all modules
+
+        cur_params = self._transformer[layer_idx]
+        kv_cache = self.state_manager.get_cache(layer_idx)
+
+        hidden_states = self.qkv(hidden_states, cur_params.qkv_w, b=cur_params.qkv_b)
+        hidden_states = self.attn(hidden_states, kv_cache, ragged_batch_info)
+        hidden_states = self.attn_out(hidden_states, cur_params.attn_out_w, b=None)
+
+        if self.tp_size > 1:
+            dist.all_reduce(hidden_states, group=self._base_mp_group)
+
+        residual, hidden_states = self.norm(residual, hidden_states, cur_params.mlp_norm_gamma, beta=None)
+
+        # Should be configurable in the future
+        hidden_states = self.mlp_1(hidden_states, cur_params.mlp_1_w, b=None)
+        hidden_states = self.mlp_2(hidden_states, cur_params.mlp_2_w, b=None)
+
+        if self.tp_size > 1:
+            dist.all_reduce(hidden_states, group=self._base_mp_group)
+
+        if layer_idx != self.num_layers - 1:
+            next_params = self._transformer[layer_idx + 1]
+            residual, hidden_states = self.norm(residual, hidden_states, next_params.attn_norm_gamma, beta=None)
+        else:
+            # On last layer, we just need to perform the residual add. Adding into the residual
+            # here is safe.
+            residual.add_(hidden_states)
+
+        return residual, hidden_states
+
+    def _forward_unembed(self, hidden_states: torch.Tensor, ragged_batch_info: RaggedBatchWrapper) -> torch.Tensor:
+        """
+        Performs unembedding of the hidden states to logits. This will only sample the final
+        token of each sequence.
+        """
+        logits = self.unembed(hidden_states,
+                              self._non_transformer.word_unembed,
+                              ragged_batch_info,
+                              gamma=self._non_transformer.final_norm)
+
+        if self.tp_size > 1:
+            comm_buffer = empty_from(self._comm_logits, (self.tp_size, logits.shape[0], logits.shape[1]))
+            full_logits = empty_from(self._return_logits, (logits.shape[0], self.vocab_size))
+
+            dist.all_gather_into_tensor(comm_buffer, logits, group=self._base_mp_group)
+
+            full_logits.copy_(comm_buffer.permute(1, 0, 2).reshape(logits.shape[0], self.vocab_size))
+
+            return full_logits
+        else:
+            return logits
+
+    def forward(self, wrapped_batch: RaggedBatchWrapper) -> torch.Tensor:
+
+        residual = self._forward_embed(wrapped_batch)
+
+        residual, hidden_states = self.norm(residual, None, self._transformer[0].attn_norm_gamma, beta=None)
+
+        for layer_idx in range(self.num_layers):
+            residual, hidden_states = self._forward_transformer_layer(layer_idx, residual, hidden_states,
+                                                                      wrapped_batch)
+
+        return self._forward_unembed(residual, wrapped_batch)