Refactor custom ops into proper file locations (quic#55)

* Refactor custom ops into proper file locations Signed-off-by: Ilango Rajagopal <[email protected]> * Remove the old native RMSNorm code Signed-off-by: Ilango Rajagopal <[email protected]> --------- Signed-off-by: Ilango Rajagopal <[email protected]>
abukhoy · Jul 10, 2024 · 8265edf · 8265edf
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diff --git a/QEfficient/customop/CMakeLists.txt b/QEfficient/customop/CMakeLists.txt
diff --git a/QEfficient/customop/CustomRMSNorm/src/customrmsnorm_aic.cpp b/QEfficient/customop/CustomRMSNorm/src/customrmsnorm_aic.cpp
diff --git a/QEfficient/customop/CustomRMSNorm/src/customrmsnorm_functions.cpp b/QEfficient/customop/CustomRMSNorm/src/customrmsnorm_functions.cpp
diff --git a/QEfficient/customop/README.md b/QEfficient/customop/README.md
diff --git a/QEfficient/customop/__init__.py b/QEfficient/customop/__init__.py
@@ -5,120 +5,7 @@
 #
 # -----------------------------------------------------------------------------
 
-"""
-RMS Norm CustomOp Node in com.qti.aisw.onnx Domain for Cloud AI 100
-This is to handle the FP16 Overflow seen in RMS Norm for LLMs
+from QEfficient.customop.ctx_scatter_gather import CtxGatherFunc, CtxScatterFunc
+from QEfficient.customop.rms_norm import CustomRMSNormAIC
 
-"""
-
-import onnxscript
-import torch
-from onnxscript.onnx_opset import opset13 as ops
-from torch import nn
-
-opset_version = 13
-custom_opset = onnxscript.values.Opset(domain="com.qti.aisw.onnx", version=1)
-
-
-# Version 1
-@onnxscript.script(custom_opset)
-def CustomRMSNorm(hidden_states: onnxscript.FLOAT, weight: onnxscript.FLOAT, epsilon: float):
-    weight = ops.Cast(weight, to=1)
-    variance = ops.ReduceMean(ops.Pow(hidden_states, 2), axes=[-1], keepdims=1)
-    epsilon = ops.Expand(epsilon, ops.Shape(variance))
-    hidden_states = hidden_states * ops.Reciprocal(ops.Sqrt(variance + epsilon))
-    return weight * hidden_states
-
-
-class CustomRMSNormOp(torch.autograd.Function):
-    @staticmethod
-    def forward(hidden_states: torch.Tensor, weight: torch.Tensor, epsilon: float):
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + epsilon)
-        return weight * hidden_states
-
-    @staticmethod
-    def setup_context(ctx, inputs, outputs):
-        pass
-
-    @staticmethod
-    def symbolic(
-        g: torch.onnx._internal.jit_utils.GraphContext,
-        hidden_states: torch.Value,
-        weight: torch.Value,
-        epsilon: torch.Value,
-    ) -> torch.Value:
-        return g.onnxscript_op(CustomRMSNorm, hidden_states, weight, epsilon_f=epsilon).setTypeAs(hidden_states)
-
-
-class CustomRMSNormAIC(nn.Module):
-    def __init__(self, hidden_size, eps=1e-05):
-        super(CustomRMSNormAIC, self).__init__()
-        self.variance_epsilon = eps
-        self.weight = torch.nn.Parameter(torch.ones(hidden_size))
-
-    def forward(self, hidden_states):
-        output = CustomRMSNormOp.apply(hidden_states, self.weight, self.variance_epsilon)
-        return output
-
-
-@onnxscript.script(onnxscript.values.Opset("com.qualcomm.cloud", 1))
-def CtxScatter(data: onnxscript.FLOAT, position_ids: onnxscript.INT32, updates: onnxscript.FLOAT) -> onnxscript.FLOAT:
-    # Find dims
-    batch_size = ops.Gather(ops.Shape(data), [0])
-    num_heads = ops.Gather(ops.Shape(data), [1])
-    seq_len = ops.Gather(ops.Shape(position_ids), [1])
-
-    # Expanded shape to create indices
-    zero = ops.Constant(value_ints=[0])
-    one = ops.Constant(value_ints=[1])
-    exp_shape = ops.Concat(batch_size, num_heads, seq_len, one, axis=0)
-
-    # Create indices
-    batch_idx = ops.Expand(ops.Unsqueeze(ops.Range(zero, batch_size, one), [1, 2, 3]), exp_shape)
-    head_idx = ops.Expand(ops.Unsqueeze(ops.Range(zero, num_heads, one), [0, 2, 3]), exp_shape)
-    ctx_idx = ops.Expand(ops.Unsqueeze(position_ids, [1, 3]), exp_shape)
-    indices = ops.Concat(batch_idx, head_idx, ctx_idx, axis=3)
-
-    return ops.ScatterND(data, indices, updates)
-
-
-class CtxScatterFunc(torch.autograd.Function):
-    @staticmethod
-    def forward(data: torch.Tensor, position_ids: torch.Tensor, updates: torch.Tensor):
-        batch_idx = torch.arange(data.shape[0]).view(-1, 1, 1)
-        head_idx = torch.arange(data.shape[1]).view(1, -1, 1)
-        ctx_idx = position_ids.unsqueeze(1)
-        data[batch_idx, head_idx, ctx_idx] = updates
-        return data
-
-    @staticmethod
-    def setup_context(ctx, inputs, outputs):
-        pass
-
-    @staticmethod
-    def symbolic(g: torch.Graph, data: torch.Value, position_ids: torch.Value, updates: torch.Value) -> torch.Value:
-        return g.onnxscript_op(CtxScatter, data, position_ids, updates).setTypeAs(data)
-
-
-@onnxscript.script(onnxscript.values.Opset("com.qualcomm.cloud", 1))
-def CtxGather(data: onnxscript.FLOAT, ctx_indices: onnxscript.INT32) -> onnxscript.FLOAT:
-    ctx_indices = ops.Expand(ctx_indices, ops.Slice(ops.Shape(data), starts=[0], ends=[3], axes=[0]))
-    ctx_indices = ops.Unsqueeze(ctx_indices, [-1])
-    return ops.GatherND(data, ctx_indices, batch_dims=2)
-
-
-class CtxGatherFunc(torch.autograd.Function):
-    @staticmethod
-    def forward(data: torch.Tensor, ctx_indices: torch.Tensor):
-        batch_indices = torch.arange(data.shape[0]).view(-1, 1, 1)
-        head_indices = torch.arange(data.shape[1]).view(1, -1, 1)
-        return data[batch_indices, head_indices, ctx_indices]
-
-    @staticmethod
-    def setup_context(ctx, inputs, outputs):
-        pass
-
-    @staticmethod
-    def symbolic(g: torch.Graph, data: torch.Value, ctx_indices: torch.Value) -> torch.Value:
-        return g.onnxscript_op(CtxGather, data, ctx_indices).setTypeAs(data)
+__all__ = ["CtxGatherFunc", "CtxScatterFunc", "CustomRMSNormAIC"]
diff --git a/QEfficient/customop/ctx_scatter_gather.py b/QEfficient/customop/ctx_scatter_gather.py
@@ -0,0 +1,81 @@
+# -----------------------------------------------------------------------------
+#
+# Copyright (c) 2023-2024 Qualcomm Innovation Center, Inc. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# -----------------------------------------------------------------------------
+
+import onnxscript
+import torch
+
+ops = onnxscript.opset13
+
+
+@onnxscript.script(onnxscript.values.Opset("com.qualcomm.cloud", 1))
+def CtxScatter(data: onnxscript.FLOAT, position_ids: onnxscript.INT32, updates: onnxscript.FLOAT) -> onnxscript.FLOAT:
+    # Find dims
+    batch_size = ops.Gather(ops.Shape(data), [0])
+    num_heads = ops.Gather(ops.Shape(data), [1])
+    seq_len = ops.Gather(ops.Shape(position_ids), [1])
+
+    # Expanded shape to create indices
+    zero = ops.Constant(value_ints=[0])
+    one = ops.Constant(value_ints=[1])
+    exp_shape = ops.Concat(batch_size, num_heads, seq_len, one, axis=0)
+
+    # Create indices
+    batch_idx = ops.Expand(ops.Unsqueeze(ops.Range(zero, batch_size, one), [1, 2, 3]), exp_shape)
+    head_idx = ops.Expand(ops.Unsqueeze(ops.Range(zero, num_heads, one), [0, 2, 3]), exp_shape)
+    ctx_idx = ops.Expand(ops.Unsqueeze(position_ids, [1, 3]), exp_shape)
+    indices = ops.Concat(batch_idx, head_idx, ctx_idx, axis=3)
+
+    return ops.ScatterND(data, indices, updates)
+
+
+class CtxScatterFunc(torch.autograd.Function):
+    """
+    Function to scatter the current key values into KV-cache.
+    """
+
+    @staticmethod
+    def forward(data: torch.Tensor, position_ids: torch.Tensor, updates: torch.Tensor):
+        batch_idx = torch.arange(data.shape[0]).view(-1, 1, 1)
+        head_idx = torch.arange(data.shape[1]).view(1, -1, 1)
+        ctx_idx = position_ids.unsqueeze(1)
+        data[batch_idx, head_idx, ctx_idx] = updates
+        return data
+
+    @staticmethod
+    def setup_context(ctx, inputs, outputs):
+        pass
+
+    @staticmethod
+    def symbolic(g: torch.Graph, data: torch.Value, position_ids: torch.Value, updates: torch.Value) -> torch.Value:
+        return g.onnxscript_op(CtxScatter, data, position_ids, updates).setTypeAs(data)
+
+
+@onnxscript.script(onnxscript.values.Opset("com.qualcomm.cloud", 1))
+def CtxGather(data: onnxscript.FLOAT, ctx_indices: onnxscript.INT32) -> onnxscript.FLOAT:
+    ctx_indices = ops.Expand(ctx_indices, ops.Slice(ops.Shape(data), starts=[0], ends=[3], axes=[0]))
+    ctx_indices = ops.Unsqueeze(ctx_indices, [-1])
+    return ops.GatherND(data, ctx_indices, batch_dims=2)
+
+
+class CtxGatherFunc(torch.autograd.Function):
+    """
+    Function to gather only the valid key values from KV-cache.
+    """
+
+    @staticmethod
+    def forward(data: torch.Tensor, ctx_indices: torch.Tensor):
+        batch_indices = torch.arange(data.shape[0]).view(-1, 1, 1)
+        head_indices = torch.arange(data.shape[1]).view(1, -1, 1)
+        return data[batch_indices, head_indices, ctx_indices]
+
+    @staticmethod
+    def setup_context(ctx, inputs, outputs):
+        pass
+
+    @staticmethod
+    def symbolic(g: torch.Graph, data: torch.Value, ctx_indices: torch.Value) -> torch.Value:
+        return g.onnxscript_op(CtxGather, data, ctx_indices).setTypeAs(data)