Support of Grok1 Model

QEfficient/base/pytorch_transforms.py

@@ -107,6 +107,10 @@ def apply(cls, model: nn.Module) -> Tuple[nn.Module, bool]:
             ):
                 for orig_method_name, mapped_method in repl_method_map.items():
                     setattr(module, orig_method_name, MethodType(mapped_method, module))
+
+                    if hasattr(module, "__qeff_init__"):
+                        module.__qeff_init__()
+
                     transformed = True
 
         return model, transformed

QEfficient/transformers/models/grok_1/__init__.py

@@ -0,0 +1,7 @@
+# -----------------------------------------------------------------------------
+#
+# Copyright (c) 2024 Qualcomm Innovation Center, Inc. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# -----------------------------------------------------------------------------
+

QEfficient/transformers/models/grok_1/modeling_grok1.py

@@ -0,0 +1,356 @@
+# -----------------------------------------------------------------------------
+#
+# Copyright (c) 2024 Qualcomm Innovation Center, Inc. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# -----------------------------------------------------------------------------
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.modeling_outputs import (
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+)
+from transformers.models.llama.modeling_llama import repeat_kv
+
+from QEfficient.customop.rms_norm import CustomRMSNormFunc
+from QEfficient.transformers.cache_utils import QEffDynamicCache
+from QEfficient.transformers.modeling_attn_mask_utils import _create_causal_mask
+from QEfficient.transformers.models.llama.modeling_llama import qeff_apply_rotary_pos_emb
+
+
+class QEFFGrok1CustomRMSNormAIC(nn.Module):
+    """
+    RMSNorm module that works by replacing the current module with compiler known custom-op.
+    """
+
+    def forward(self, hidden_states):
+        return CustomRMSNormFunc.apply(
+            hidden_states, self.scale, self.variance_epsilon if hasattr(self, "variance_epsilon") else self.eps
+        )
+
+
+class QEffGrok1MultiHeadAttention(nn.Module):
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        layer_idx: int,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        batch_index: Optional[torch.LongTensor] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len = past_key_value.get_usable_length(kv_seq_len, layer_idx)
+
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = qeff_apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "batch_index": batch_index,
+                "position_ids": position_ids,
+            }  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)).to(torch.float)
+        attn_weights = attn_weights * self.attn_output_multiplier
+        attn_weights = self.max_attn_val * F.tanh(attn_weights / self.max_attn_val)
+
+        if attention_mask is not None:
+            attn_weights = torch.where(attention_mask, torch.tensor(-10000.0, dtype=torch.float32), attn_weights)
+
+        attn_weights = F.softmax(attn_weights, dim=-1).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, -1)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class QEffGrok1MoeBlock(nn.Module):
+    def forward(self, hidden_states: torch.Tensor):
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        router_logits = self.gate(hidden_states)
+
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+        # Creating experts mask and routing weights masked
+        awesome_experts_mask_1 = (
+            torch.nn.functional.one_hot(selected_experts[:, 0], num_classes=self.num_experts).bool().T.unsqueeze(-1)
+        )
+        awesome_experts_mask_2 = (
+            torch.nn.functional.one_hot(selected_experts[:, 1], num_classes=self.num_experts).bool().T.unsqueeze(-1)
+        )
+
+        gateupout1 = torch.zeros(hidden_states.shape[0], 32768)  # T, hs
+        gateupout2 = torch.zeros(hidden_states.shape[0], 32768)  # T, hs
+        for expert_idx in range(self.num_experts):
+            expert_layer = self.experts[expert_idx]
+            current_expert_output = expert_layer.act_fn(expert_layer.linear(hidden_states)) * expert_layer.linear_v(
+                hidden_states
+            )
+            gateupout1 += torch.where(
+                awesome_experts_mask_1[expert_idx], current_expert_output, torch.zeros_like(gateupout1)
+            )
+            gateupout2 += torch.where(
+                awesome_experts_mask_2[expert_idx], current_expert_output, torch.zeros_like(gateupout2)
+            )
+
+        downout1 = torch.zeros_like(hidden_states)
+        downout2 = torch.zeros_like(hidden_states)
+        concat_mask = torch.cat((awesome_experts_mask_1.unsqueeze(0), awesome_experts_mask_2.unsqueeze(0)), dim=0)
+        concat_down = torch.cat((downout1.unsqueeze(0), downout2.unsqueeze(0)), dim=0)
+        concat_gateout = torch.cat((gateupout1.unsqueeze(0), gateupout2.unsqueeze(0)), dim=0)
+        for expert_idx in range(self.num_experts):
+            expert_layer = self.experts[expert_idx]
+            concat_down += torch.where(
+                concat_mask[:, expert_idx, :], expert_layer.linear_1(concat_gateout), torch.zeros_like(concat_down)
+            )
+
+        downout1, downout2 = concat_down[0], concat_down[1]
+        hidden_states = (
+            downout1 * routing_weights[:, 0].unsqueeze(-1) + downout2 * routing_weights[:, 1].unsqueeze(-1)
+        ).reshape(batch_size, sequence_length, hidden_dim)
+
+        return hidden_states, router_logits
+
+
+class QEffGrok1DecoderLayer(nn.Module):
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        batch_index: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = False,
+        output_router_logits: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        residual = hidden_states
+        hidden_states = self.pre_attn_norm(hidden_states)
+        hidden_states, attention_weights, present_key_value = self.attn(
+            hidden_states,
+            layer_idx=self.layer_idx,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            batch_index=batch_index,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = self.post_attn_norm(hidden_states)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.pre_moe_norm(hidden_states)
+        hidden_states, router_logits = self.moe_block(hidden_states)
+        hidden_states = self.post_moe_norm(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+        if output_attentions:
+            outputs += (attention_weights,)
+        if use_cache:
+            outputs += (present_key_value,)
+        if output_router_logits:
+            outputs += (router_logits,)
+        return outputs
+
+
+class QEffGrok1Model(nn.Module):
+    def __qeff_init__(self):
+        for idx, layer in enumerate(self.layers):
+            layer.layer_idx = idx
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        batch_index: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_router_logits: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MoeModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        past_key_values = QEffDynamicCache.from_legacy_cache(past_key_values)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+            inputs_embeds = inputs_embeds * self.embedding_multiplier_scale
+
+        attention_mask = _create_causal_mask(position_ids=position_ids, target_length=past_key_values_length)
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        all_router_logits = () if output_router_logits else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                batch_index=batch_index,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+            if output_router_logits:
+                all_router_logits += (layer_outputs[-1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        past_key_values = past_key_values.to_legacy_cache()
+
+        return MoeModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            router_logits=all_router_logits,
+        )
+
+
+class QEffGrok1ModelForCausalLM(nn.Module):
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        batch_index: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_router_logits: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ):
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            batch_index=batch_index,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            output_router_logits=output_router_logits,
+            return_dict=return_dict,
+            **kwargs,
+        )
+
+        # Cast to int32 to avoid ONNXRT issue
+        logit_idx = position_ids.to(torch.int32).argmax(1, keepdim=True)
+        hidden_states = outputs[0][torch.arange(position_ids.shape[0]).view(-1, 1), logit_idx]
+        logits = self.lm_head(hidden_states)
+        logits = logits * self.output_multiplier_scale
+        logits = logits.float()
+
+        return MoeCausalLMOutputWithPast(
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_logits=outputs.router_logits,
+        )

QEfficient/transformers/models/modeling_auto.py

@@ -1291,6 +1291,7 @@ class QEFFAutoModelForCausalLM(QEFFBaseModel):
         FP8DeQuantLinearToLinearTransform,
         CustomOpsTransform,
         KVCacheTransform,
+        KVCacheModuleMethodMapperTransform,
     ]
     _onnx_transforms = [FP16ClipTransform, SplitTensorsTransform]
 

QEfficient/transformers/models/pytorch_transforms.py

@@ -189,6 +189,14 @@
     QEffGraniteMoeRotaryEmbedding,
     QEffGraniteMoeTopKGating,
 )
+from QEfficient.transformers.models.grok_1.modeling_grok1 import (
+    QEFFGrok1CustomRMSNormAIC,
+    QEffGrok1DecoderLayer,
+    QEffGrok1Model,
+    QEffGrok1ModelForCausalLM,
+    QEffGrok1MoeBlock,
+    QEffGrok1MultiHeadAttention,
+)
 from QEfficient.transformers.models.internvl.modeling_internvl import QEffInternVisionEmbeddings, QEffInternVLModel
 from QEfficient.transformers.models.llama.modeling_llama import (
     QEffLlamaAttention,
@@ -468,5 +476,20 @@ class KVCacheModuleMethodMapperTransform(ModuleMethodMapperTransform):
             "get_qeff_language_decoder": QEffInternVLModel.get_qeff_language_decoder,
         },
         "InternVisionEmbeddings": {"forward": QEffInternVisionEmbeddings.forward},
+        # Mapping for grok1 model
+        "Grok1ModelForCausalLM": {"forward": QEffGrok1ModelForCausalLM.forward},
+        "Grok1Model": {
+            "forward": QEffGrok1Model.forward,
+            "__qeff_init__": QEffGrok1Model.__qeff_init__,
+        },
+        "DecoderLayer": {"forward": QEffGrok1DecoderLayer.forward},
+        "MoeBlock": {"forward": QEffGrok1MoeBlock.forward},
+        "MultiHeadAttention": {
+            "forward": QEffGrok1MultiHeadAttention.forward,
+        },
+        "RMSNorm": {
+            "forward": QEFFGrok1CustomRMSNormAIC.forward,
+        },
     }
+
     _match_class_replace_method = {}