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venv/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/common_parameters/__init__.py

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+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from .attn_output_parameters import *
+from .embedding_parameters import *
+from .mlp_parameters import *
+from .moe_parameters import *
+from .norm_parameters import *
+from .qkv_parameters import *
+from .unembed_parameters import *
+from .invfreq_parameters import *

venv/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/common_parameters/mlp_parameters.py

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+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import torch
+
+from ...model_implementations.parameter_base import ParameterBase
+"""
+MLP Parameter Containers
+"""
+
+
+class MLP1Parameter(ParameterBase):
+    """
+    First MLP projection weight container. This performs a straight pass-through to the
+    model implementation for transformation.
+    """
+    params: torch.Tensor
+
+    def finalize(self) -> torch.Tensor:
+        # NOTE(cmikeh2): If we are gated but not in the format specified below, we should trigger a permutation here.
+        # I am not currently aware of any models that use this format (or how we should even detect it; probably should
+        # just be a different param entirely, but until then we'll just assume the format is correct).
+        return self.inference_model.transform_mlp_1_param(self.params)
+
+
+class GatedMLPParameter(ParameterBase):
+    """
+    Gated MLP projection container.
+    """
+
+    gate_params: torch.Tensor
+    """
+    Weight parameter for the gating matrix.
+    """
+
+    up_params: torch.Tensor
+    """
+    For lack of a better name, the non-gating weight parameters.
+    """
+
+    def finalize(self) -> torch.Tensor:
+        """
+        Our gated format (this is different from InferenceV1!) is to have the gate and activated neurons
+        interleaved. So if we have 4 output neurons (two effective neurons) with 4 input neurons, the finalized
+        parameter will look like:
+        [g0_0, g0_1, g0_2, g0_3]
+        [a0_0, a0_1, a0_2, a0_3]
+        [g1_0, g1_1, g1_2, g1_3]
+        [a1_0, a1_1, a1_2, a1_3]
+
+        As a reference, in inference v1, the format is:
+        [g0_0, g0_1, g0_2, g0_3]
+        [g1_0, g1_1, g1_2, g1_3]
+        [a0_0, a0_1, a0_2, a0_3]
+        [a1_0, a1_1, a1_2, a1_3]
+        """
+        assert self.gate_params.shape[0] == self.up_params.shape[
+            0], "Gated MLP parameters must have the same number of neurons."
+        total_neurons = self.gate_params.shape[0] + self.up_params.shape[0]
+
+        # flip the order if even with the correct tokenizer we get wrong output
+        #fused_param = torch.cat([self.up_params, self.gate_params], dim=-1).reshape(total_neurons, -1)
+        fused_param = torch.cat([self.gate_params, self.up_params], dim=-1).reshape(total_neurons, -1)
+        return self.inference_model.transform_mlp_1_param(fused_param)
+
+
+class MLP2Parameter(ParameterBase):
+    """
+    Second MLP projection weight container. This performs a straight pass-through to the
+    model implementation for transformation.
+    """
+
+    params: torch.Tensor
+    """
+    Full weight parameter.
+    """
+
+    def finalize(self) -> torch.Tensor:
+        return self.inference_model.transform_mlp_2_param(self.params)

venv/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/common_parameters/norm_parameters.py

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+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import torch
+
+from ...model_implementations.parameter_base import ParameterBase
+"""
+Common Attention Output Parameter Patterns
+"""
+
+
+class NormParameter(ParameterBase):
+    """
+    Simple normalization container.
+    """
+
+    params: torch.Tensor
+
+    def finalize(self) -> torch.Tensor:
+        return self.inference_model.transform_norm_param(self.params)

venv/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/common_parameters/qkv_parameters.py

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+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import torch
+
+from ...model_implementations.parameter_base import ParameterBase
+"""
+Common QKV Parameter Patterns
+"""
+
+
+class FusedQKVParameter(ParameterBase):
+    """
+    Traditional fused QKV parameters for QKV projection. This is functionally
+    a direct copy.
+
+    src_qkv_w shape: [3 * out_features, in_features]
+    qkv_w shape: [3 * out_features, in_features]
+    """
+
+    params: torch.Tensor
+
+    def finalize(self) -> torch.Tensor:
+        return self.inference_model.transform_qkv_param(self.params)
+
+
+class UnfusedQKVParameter(ParameterBase):
+    """
+    QKV parameter container for unfused QKV projection.
+
+    src_param shapes: 3 x [out_features, in_features]
+    dst_param shape: [3 x out_features, in_features]
+    """
+
+    q_params: torch.Tensor
+
+    k_params: torch.Tensor
+
+    v_params: torch.Tensor
+
+    def finalize(self):
+        fused_param = torch.cat([self.q_params, self.k_params, self.v_params], dim=0)
+        return self.inference_model.transform_qkv_param(fused_param)
+
+
+def megatron_qkv_reshape(param: torch.Tensor, head_size: int, n_heads: int) -> torch.Tensor:
+    assert param.shape[0] == 3 * n_heads * head_size
+
+    all_heads = torch.chunk(param, chunks=3 * n_heads, dim=0)
+    q_heads = all_heads[::3]
+    k_heads = all_heads[1::3]
+    v_heads = all_heads[2::3]
+    return torch.cat([q_heads, k_heads, v_heads], dim=0)
+
+
+class MegatronQKVParameter(ParameterBase):
+    """
+    QKV parameter container for Megatron-style QKV projection. Megatron stores the parameter
+    as [n_heads, 3, head_size, in_features] whereas our inference system is built around
+    [3, n_heads, head_size, in_features]. This container handles the conversion.
+
+    Note: this container expects the model implementation to implement properties for
+    `head_size` and `n_heads`.
+
+    src_qkv_w shape: [3 * out_features, in_features]
+    qkv_w shape: [3 * out_features, in_features]
+    """
+
+    params: torch.Tensor
+
+    def finalize(self) -> torch.Tensor:
+        head_size = self.inference_model.head_size
+        n_heads = self.inference_model.n_heads
+
+        transposed_param = megatron_qkv_reshape(self.params, head_size, n_heads)
+        return self.inference_model.transform_qkv_param(transposed_param)
+
+
+def transform_gqa_megatron(src_param: torch.Tensor, head_size: int, n_q_heads: int, n_kv_heads: int) -> torch.Tensor:
+    assert src_param.shape[0] == (2 * n_kv_heads + n_q_heads) * head_size
+
+    head_ratio = n_q_heads // n_kv_heads
+
+    # Reshape to get the groups as the leading dimension
+    groups_leading_view = src_param.reshape(n_kv_heads, 2 + head_ratio, head_size, -1)
+    q_heads = groups_leading_view[:, :head_ratio, :, :].reshape(-1, groups_leading_view.shape[-1])
+    k_heads = groups_leading_view[:, head_ratio, :, :].reshape(-1, groups_leading_view.shape[-1])
+    v_heads = groups_leading_view[:, head_ratio + 1, :, :].reshape(-1, groups_leading_view.shape[-1])
+    # Squeeze will remove extra dimension for bias
+    return torch.cat([q_heads, k_heads, v_heads], dim=0).squeeze()
+
+
+class GQAMegatronQKVParameter(ParameterBase):
+    """
+    QKV parameter for Megatron-style QKV projection with GQA-style QKV projection. In this
+    storage format each of the groups is stored consecutively, so there will be multiple q_heads,
+    then one k head, and one v head.
+
+    Note: this container expects the model implementation to implement properties for
+    `head_size`, `n_q_heads`, and `n_kv_heads`.
+
+    src_qkv_w shape: [(2 * n_kv_heads + n_q_heads) * head_size, in_features]
+    qkv_w shape: [(2 * n_kv_heads + n_q_heads) * head_size, in_features]
+    """
+
+    params: torch.Tensor
+
+    def finalize(self) -> torch.Tensor:
+        head_size = self.inference_model.head_size
+        n_q_heads = self.inference_model.n_heads_q
+        n_kv_heads = self.inference_model.n_heads_kv
+        transposed_param = transform_gqa_megatron(self.params, head_size, n_q_heads, n_kv_heads)
+        return self.inference_model.transform_qkv_param(transposed_param)

venv/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/falcon/__init__.py

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+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from .policy import FalconPolicy

venv/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/falcon/container.py

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+# 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 Falcon 7b model looks like this:
+
+FalconForCausalLM(
+  (transformer): FalconModel(
+    (word_embeddings): Embedding(65024, 4544)
+    (h): ModuleList(
+      (0-31): 32 x FalconDecoderLayer(
+        (self_attention): FalconAttention(
+          (maybe_rotary): FalconRotaryEmbedding()
+          (query_key_value): FalconLinear(in_features=4544, out_features=4672, bias=False)
+          (dense): FalconLinear(in_features=4544, out_features=4544, bias=False)
+          (attention_dropout): Dropout(p=0.0, inplace=False)
+        )
+        (mlp): FalconMLP(
+          (dense_h_to_4h): FalconLinear(in_features=4544, out_features=18176, bias=False)
+          (act): GELU(approximate='none')
+          (dense_4h_to_h): FalconLinear(in_features=18176, out_features=4544, bias=False)
+        )
+        (input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)
+      )
+    )
+    (ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)
+  )
+  (lm_head): Linear(in_features=4544, out_features=65024, bias=False)
+)
+'''
+
+
+class FalconTransformerContainer(LayerContainer):
+    """
+        Transformer layer container for the Falcon model.
+    """
+    qkv_w: FusedQKVParameter
+    attn_out_w: AttentionOutputParameter
+    mlp_1_w: MLP1Parameter
+    mlp_2_w: MLP2Parameter
+    ln_attn_gamma: NormParameter
+    ln_attn_beta: NormParameter
+
+    PARAM_MAPPING = {
+        "self_attention.query_key_value.weight": "qkv_w.params",
+        "self_attention.dense.weight": "attn_out_w.params",
+        "mlp.dense_h_to_4h.weight": "mlp_1_w.params",
+        "mlp.dense_4h_to_h.weight": "mlp_2_w.params",
+        "input_layernorm.weight": "ln_attn_gamma.params",
+        "input_layernorm.bias": "ln_attn_beta.params",
+    }
+
+
+class FalconNonTransformerContainer(LayerContainer):
+    """
+        Non-Transformer layer container for the Falcon model.
+    """
+    word_emb: EmbeddingParameter
+    word_unembed: UnembedParameter
+    final_norm_gamma: NormParameter
+    final_norm_beta: NormParameter
+
+    PARAM_MAPPING = {
+        "transformer.word_embeddings.weight": "word_emb.params",
+        "transformer.ln_f.weight": "final_norm_gamma.params",
+        "transformer.ln_f.bias": "final_norm_beta.params",
+        "lm_head.weight": "word_unembed.params",
+    }
+
+
+'''
+ # HF Falcon 40b model looks like this:
+
+ FalconForCausalLM(
+  (transformer): FalconModel(
+    (word_embeddings): Embedding(65024, 8192)
+    (h): ModuleList(
+      (0-59): 60 x FalconDecoderLayer(
+        (self_attention): FalconAttention(
+          (maybe_rotary): FalconRotaryEmbedding()
+          (query_key_value): FalconLinear(in_features=8192, out_features=9216, bias=False)
+          (dense): FalconLinear(in_features=8192, out_features=8192, bias=False)
+          (attention_dropout): Dropout(p=0.0, inplace=False)
+        )
+        (mlp): FalconMLP(
+          (dense_h_to_4h): FalconLinear(in_features=8192, out_features=32768, bias=False)
+          (act): GELU(approximate='none')
+          (dense_4h_to_h): FalconLinear(in_features=32768, out_features=8192, bias=False)
+        )
+        (ln_attn): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
+        (ln_mlp): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
+      )
+    )
+    (ln_f): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
+  )
+  (lm_head): Linear(in_features=8192, out_features=65024, bias=False)
+)
+'''
+
+
+class FalconNewArchTransformerContainer(LayerContainer):
+    """
+        Transformer layer container for the Falcon model.
+    """
+    qkv_w: GQAMegatronQKVParameter
+    attn_out_w: AttentionOutputParameter
+    mlp_1_w: MLP1Parameter
+    mlp_2_w: MLP2Parameter
+    ln_attn_gamma: NormParameter
+    ln_attn_beta: NormParameter
+    ln_mlp_gamma: NormParameter
+    ln_mlp_beta: NormParameter
+
+    PARAM_MAPPING = {
+        "self_attention.query_key_value.weight": "qkv_w.params",
+        "self_attention.dense.weight": "attn_out_w.params",
+        "mlp.dense_h_to_4h.weight": "mlp_1_w.params",
+        "mlp.dense_4h_to_h.weight": "mlp_2_w.params",
+        "ln_attn.weight": "ln_attn_gamma.params",
+        "ln_attn.bias": "ln_attn_beta.params",
+        "ln_mlp.weight": "ln_mlp_gamma.params",
+        "ln_mlp.bias": "ln_mlp_beta.params",
+    }

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

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+# 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)

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

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

venv/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/mixtral/container.py

@@ -0,0 +1,46 @@
+# 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 deepspeed.inference.v2.model_implementations.common_parameters import *
+from deepspeed.inference.v2.model_implementations.layer_container_base import LayerContainer
+
+
+class MixtralTransformerContainer(LayerContainer):
+
+    qkv_w: UnfusedQKVParameter
+    attn_out_w: AttentionOutputParameter
+    moe_gate: MoEGatingWeightParameter
+    moe_mlp_1: UnfusedMoEGatedMLPParameter
+    moe_mlp_2: UnfusedMoEMLP2Parameter
+    attn_norm_gamma: NormParameter
+    mlp_norm_gamma: NormParameter
+
+    PARAM_MAPPING = {
+        "input_layernorm.weight": "attn_norm_gamma.params",
+        "post_attention_layernorm.weight": "mlp_norm_gamma.params",
+        "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.o_proj.weight": "attn_out_w.params",
+        "block_sparse_moe.gate.weight": "moe_gate.params",
+        "block_sparse_moe.experts.*.w1.weight": "moe_mlp_1.gating_experts",
+        "block_sparse_moe.experts.*.w3.weight": "moe_mlp_1.up_experts",
+        "block_sparse_moe.experts.*.w2.weight": "moe_mlp_2.experts",
+    }
+
+
+class MixtralNonTransformerContainer(LayerContainer):
+
+    word_emb: EmbeddingParameter
+    word_unembed: UnembedParameter
+    final_norm: NormParameter
+
+    PARAM_MAPPING = {
+        "model.embed_tokens.weight": "word_emb.params",
+        "lm_head.weight": "word_unembed.params",
+        "model.norm.weight": "final_norm.params",
+    }

venv/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)

venv/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

venv/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

venv/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",
+    }

venv/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)

venv/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

venv/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/phi/__init__.py

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

venv/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/phi/containers.py

@@ -0,0 +1,91 @@
+# 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 Phi-2 model looks like this:
+
+PhiForCausalLM(
+  (model): PhiModel(
+    (embed_tokens): Embedding(51200, 2560)
+    (embed_dropout): Dropout(p=0.0, inplace=False)
+    (layers): ModuleList(
+      (0-31): 32 x PhiDecoderLayer(
+        (self_attn): PhiAttention(
+          (q_proj): Linear(in_features=2560, out_features=2560, bias=True)
+          (k_proj): Linear(in_features=2560, out_features=2560, bias=True)
+          (v_proj): Linear(in_features=2560, out_features=2560, bias=True)
+          (dense): Linear(in_features=2560, out_features=2560, bias=True)
+          (rotary_emb): PhiRotaryEmbedding()
+        )
+        (mlp): PhiMLP(
+          (activation_fn): NewGELUActivation()
+          (fc1): Linear(in_features=2560, out_features=10240, bias=True)
+          (fc2): Linear(in_features=10240, out_features=2560, bias=True)
+        )
+        (input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
+        (resid_dropout): Dropout(p=0.1, inplace=False)
+      )
+    )
+    (final_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
+  )
+  (lm_head): Linear(in_features=2560, out_features=51200, bias=True)
+)
+'''
+
+
+class PhiTransformerContainer(LayerContainer):
+    """
+        Transformer layer container for the Phi 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
+    ln_gamma: NormParameter
+    ln_beta: 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.dense.weight": "attn_out_w.params",
+        "self_attn.dense.bias": "attn_out_b.params",
+        "mlp.fc1.weight": "mlp_1_w.params",
+        "mlp.fc1.bias": "mlp_1_b.params",
+        "mlp.fc2.weight": "mlp_2_w.params",
+        "mlp.fc2.bias": "mlp_2_b.params",
+        "input_layernorm.weight": "ln_gamma.params",
+        "input_layernorm.bias": "ln_beta.params",
+    }
+
+
+class PhiNonTransformerContainer(LayerContainer):
+    """
+        Non-Transformer layer container for the Phi model.
+    """
+    word_emb: EmbeddingParameter
+    word_unembed_w: UnembedParameter
+    word_unembed_b: UnembedParameter
+    final_norm_gamma: NormParameter
+    final_norm_beta: NormParameter
+
+    PARAM_MAPPING = {
+        "model.embed_tokens.weight": "word_emb.params",
+        "model.final_layernorm.weight": "final_norm_gamma.params",
+        "model.final_layernorm.bias": "final_norm_beta.params",
+        "lm_head.weight": "word_unembed_w.params",
+        "lm_head.bias": "word_unembed_b.params",
+    }

venv/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/phi/model.py

@@ -0,0 +1,199 @@
+# 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 .containers import PhiNonTransformerContainer, PhiTransformerContainer
+
+
+class PhiInferenceModel(DSTransformerModelBase):
+    """
+    Inference model implementation for ragged batching for Llama-2 models.
+    """
+
+    _non_transformer: Optional[PhiNonTransformerContainer]
+    """
+    Embed + unembed container. Specializing the type annotation.
+    """
+
+    _transformer: Optional[Iterable[PhiTransformerContainer]]
+    """
+    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 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:
+        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) -> Optional[RotateHalfConfig]:
+        rotary_dim = int(self._config.partial_rotary_factor * self.head_size)
+        return RotateHalfConfig(rotate_dim=rotary_dim, theta_base=self._config.rope_theta)
+
+    """
+    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.
+        """
+        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=cur_params.qkv_b)
+        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=cur_params.attn_out_b)
+
+        mlp_ln_out = hidden_states
+        mlp_hidden_state = self.mlp_1(mlp_ln_out, cur_params.mlp_1_w, b=cur_params.mlp_1_b)
+        mlp_output = self.mlp_2(mlp_hidden_state, cur_params.mlp_2_w, b=cur_params.mlp_2_b)
+
+        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_gamma, beta=next_params.ln_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_w,
+                              ragged_batch_info,
+                              bias=self._non_transformer.word_unembed_b,
+                              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_gamma,
+                                            beta=self._transformer[0].ln_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)

venv/lib/python3.10/site-packages/deepspeed/inference/v2/model_implementations/phi/policy.py

@@ -0,0 +1,32 @@
+# 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 .containers import PhiNonTransformerContainer, PhiTransformerContainer
+from .model import PhiInferenceModel
+
+
+class PhiPolicy(InferenceV2Policy):
+
+    def instantiate_model(self, engine_config: RaggedInferenceEngineConfig, mp_group: Any) -> PhiInferenceModel:
+        return PhiInferenceModel(config=self._model_config, engine_config=engine_config, base_mp_group=mp_group)
+
+    def build_container_map(self) -> ContainerMap:
+        map = ContainerMap()
+
+        trans_container_cls = PhiTransformerContainer
+        transformer_containers = [trans_container_cls(self.model) for _ in range(self.model.num_layers)]
+
+        map.set_transformer_params(['model.layers'], transformer_containers)
+
+        map.set_non_transformer_params(PhiNonTransformerContainer(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

venv/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

venv/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",
+    }

venv/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)

venv/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

venv/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