Refactor custom SDPA op to separate kv cache update from the custom sdpa op

kimishpatel · kimishpatel · commit 0008410bb174 · 2024-09-25T12:49:14.000-07:00
Pull Request resolved: pytorch/executorch#5651 ghstack-source-id: 244684194 @exported-using-ghexport Differential Revision: [D62301837](https://our.internmc.facebook.com/intern/diff/D62301837/)
diff --git a/extension/llm/custom_ops/op_sdpa.cpp b/extension/llm/custom_ops/op_sdpa.cpp
@@ -754,6 +754,152 @@ void update_cache(
   }
 }
 
+/*
+  Input params
+  @param[in] q_projected Projected query with query weights.
+  Format [n_layers, batch size, seq_len, num heads, head dim]
+  @param[in] k_projected Projected query with key weights.
+  Format [n_layers, batch size, seq_len, num heads, head dim]
+  @param[in] v_projected Projected query with value weights.
+  Format [n_layers, batch size, seq_len, num heads, head dim]
+  @param[in] key_cache Cache of previous k_projected.
+  Format [n_layers, batch size, max_seq_len, num heads, head dim]
+  @param[in] key_cache Cache of previous v_projected.
+  Format [n_layers, batch size, max_seq_len, num heads, head dim]
+  ....
+  @param[in] start_pos: sequence position
+  @param[in] seq_len: Seq length. e.g. seq_len dim of q_projected.
+*/
+Tensor& custom_sdpa_out(
+    RuntimeContext& ctx,
+    const Tensor& q,
+    const Tensor& k,
+    const Tensor& v,
+    const int64_t start_pos,
+    const int64_t seq_len,
+    const optional<Tensor>& attn_mask,
+    const double dropout_p,
+    const bool is_causal,
+    // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy
+    const optional<double> scale,
+    Tensor& output) {
+  ET_KERNEL_CHECK_MSG(
+      ctx,
+      !attn_mask.has_value() || !is_causal,
+      InvalidArgument,
+      output,
+      "attn_mask and is_causal cannot be set at the same time");
+
+  ET_CHECK_MSG(q.dim() == 4, "query must be a 4D tensor");
+
+  auto q_seq_len = q.size(1);
+
+  // Refactor the following into create_view util perhaps using
+  // TensorPtr
+  std::array<exec_aten::DimOrderType, util::kKVDim> sliced_key_dim_order{
+      0, 1, 2, 3};
+  std::array<exec_aten::SizesType, util::kKVDim> sliced_key_sizes;
+  sliced_key_sizes[0] = k.size(0);
+  sliced_key_sizes[1] = start_pos + seq_len; // key_cache.size(2);
+  sliced_key_sizes[2] = k.size(2);
+  sliced_key_sizes[3] = k.size(3);
+  std::array<exec_aten::StridesType, util::kKVDim> sliced_key_strides;
+  dim_order_to_stride_nocheck(
+      sliced_key_sizes.data(),
+      sliced_key_dim_order.data(),
+      util::kKVDim,
+      sliced_key_strides.data());
+  // since the cache is sliced, the batch stride needs to stay the same.
+  sliced_key_strides[0] = k.strides()[0];
+  void* key_cache_data = k.mutable_data_ptr();
+  TensorImpl k_impl = TensorImpl(
+      k.scalar_type(),
+      util::kKVDim,
+      sliced_key_sizes.data(),
+      key_cache_data,
+      sliced_key_dim_order.data(),
+      sliced_key_strides.data(),
+      TensorShapeDynamism::STATIC);
+  Tensor sliced_key_cache(&k_impl);
+
+  std::array<exec_aten::DimOrderType, util::kKVDim> sliced_value_dim_order{
+      0, 1, 2, 3};
+  std::array<exec_aten::SizesType, util::kKVDim> sliced_value_sizes;
+  sliced_value_sizes[0] = v.size(0);
+  sliced_value_sizes[1] = start_pos + seq_len; // value_cache.size(2);
+  sliced_value_sizes[2] = v.size(2);
+  sliced_value_sizes[3] = v.size(3);
+  std::array<exec_aten::StridesType, util::kKVDim> sliced_value_strides;
+  dim_order_to_stride_nocheck(
+      sliced_value_sizes.data(),
+      sliced_value_dim_order.data(),
+      util::kKVDim,
+      sliced_value_strides.data());
+  // since the cache is sliced, the batch stride needs to stay the same.
+  sliced_value_strides[0] = v.strides()[0];
+  void* value_cache_data = v.mutable_data_ptr();
+  TensorImpl value_impl = TensorImpl(
+      v.scalar_type(),
+      util::kKVDim,
+      sliced_value_sizes.data(),
+      value_cache_data,
+      sliced_value_dim_order.data(),
+      sliced_value_strides.data(),
+      TensorShapeDynamism::STATIC);
+  Tensor sliced_value_cache(&value_impl);
+
+  ET_KERNEL_CHECK(
+      ctx,
+      resize_tensor(output, q.sizes()) == Error::Ok,
+      InvalidArgument,
+      output);
+
+  // TODO(task): replace the template param selection logic
+  // with whatever apprpriately makes more sense for
+  ET_SWITCH_FLOAT_TYPES(q.scalar_type(), ctx, "flash_attention", CTYPE, [&] {
+    // TODO we need to re-evaluate this for ARM CPUs
+    // And there can be many so instead of templatizing
+    // we might consider another appraoch
+    if (q_seq_len >= 768) {
+      cpu_flash_attention<CTYPE, 256, 512>(
+          output,
+          q,
+          sliced_key_cache,
+          sliced_value_cache,
+          dropout_p,
+          is_causal,
+          attn_mask,
+          scale,
+          true,
+          start_pos);
+    } else if (q_seq_len >= 192) {
+      cpu_flash_attention<CTYPE, 64, 512>(
+          output,
+          q,
+          sliced_key_cache,
+          sliced_value_cache,
+          dropout_p,
+          is_causal,
+          attn_mask,
+          scale,
+          true,
+          start_pos);
+    } else {
+      cpu_flash_attention<CTYPE, 32, 512>(
+          output,
+          q,
+          sliced_key_cache,
+          sliced_value_cache,
+          dropout_p,
+          is_causal,
+          attn_mask,
+          scale,
+          true,
+          start_pos);
+    }
+  });
+  return output;
+}
 } // anonymous namespace
 
 Tensor& flash_attention_kernel_out(
@@ -860,129 +1006,24 @@ Tensor& sdpa_with_kv_cache_out(
       InvalidArgument,
       output);
 
-  ET_KERNEL_CHECK_MSG(
-      ctx,
-      !attn_mask.has_value() || !is_causal,
-      InvalidArgument,
-      output,
-      "attn_mask and is_causal cannot be set at the same time");
-
   ET_CHECK_MSG(q_projected.dim() == 4, "query must be a 4D tensor");
 
   update_cache(k_projected, key_cache, start_pos, seq_len);
   update_cache(v_projected, value_cache, start_pos, seq_len);
 
-  auto q_seq_len = q_projected.size(1);
-
-  std::array<exec_aten::DimOrderType, util::kKVDim> sliced_key_dim_order{
-      0, 1, 2, 3};
-  std::array<exec_aten::SizesType, util::kKVDim> sliced_key_sizes;
-  sliced_key_sizes[0] = key_cache.size(0);
-  sliced_key_sizes[1] = start_pos + seq_len; // key_cache.size(2);
-  sliced_key_sizes[2] = key_cache.size(2);
-  sliced_key_sizes[3] = key_cache.size(3);
-  std::array<exec_aten::StridesType, util::kKVDim> sliced_key_strides;
-  dim_order_to_stride_nocheck(
-      sliced_key_sizes.data(),
-      sliced_key_dim_order.data(),
-      util::kKVDim,
-      sliced_key_strides.data());
-  // since the cache is sliced, the batch stride needs to stay the same.
-  sliced_key_strides[0] = key_cache.strides()[0];
-  void* key_cache_data = key_cache.mutable_data_ptr();
-  TensorImpl k_impl = TensorImpl(
-      key_cache.scalar_type(),
-      util::kKVDim,
-      sliced_key_sizes.data(),
-      key_cache_data,
-      sliced_key_dim_order.data(),
-      sliced_key_strides.data(),
-      TensorShapeDynamism::STATIC);
-  Tensor sliced_key_cache(&k_impl);
-
-  std::array<exec_aten::DimOrderType, util::kKVDim> sliced_value_dim_order{
-      0, 1, 2, 3};
-  std::array<exec_aten::SizesType, util::kKVDim> sliced_value_sizes;
-  sliced_value_sizes[0] = value_cache.size(0);
-  sliced_value_sizes[1] = start_pos + seq_len; // value_cache.size(2);
-  sliced_value_sizes[2] = value_cache.size(2);
-  sliced_value_sizes[3] = value_cache.size(3);
-  std::array<exec_aten::StridesType, util::kKVDim> sliced_value_strides;
-  dim_order_to_stride_nocheck(
-      sliced_value_sizes.data(),
-      sliced_value_dim_order.data(),
-      util::kKVDim,
-      sliced_value_strides.data());
-  // since the cache is sliced, the batch stride needs to stay the same.
-  sliced_value_strides[0] = value_cache.strides()[0];
-  void* value_cache_data = value_cache.mutable_data_ptr();
-  TensorImpl value_impl = TensorImpl(
-      value_cache.scalar_type(),
-      util::kKVDim,
-      sliced_value_sizes.data(),
-      value_cache_data,
-      sliced_value_dim_order.data(),
-      sliced_value_strides.data(),
-      TensorShapeDynamism::STATIC);
-  Tensor sliced_value_cache(&value_impl);
-
-  // Is this true?
-  // Cant do this as is because the expectation of this kernel is
-  // that q, k, v are [B, num heads, seq length, head dim]
-  // and the cache is [B, max seq len, num heads, head dim]
-  // and q, k, v are all [B, seq length, num heads, head dim]
-
-  ET_KERNEL_CHECK(
+  custom_sdpa_out(
       ctx,
-      resize_tensor(output, q_projected.sizes()) == Error::Ok,
-      InvalidArgument,
+      q_projected,
+      key_cache,
+      value_cache,
+      start_pos,
+      seq_len,
+      attn_mask,
+      dropout_p,
+      is_causal,
+      scale,
       output);
 
-  // TODO(task): replace the template param selection logic
-  // with whatever apprpriately makes more sense for
-  ET_SWITCH_FLOAT_TYPES(
-      q_projected.scalar_type(), ctx, "flash_attention", CTYPE, [&] {
-        // TODO we need to re-evaluate this for ARM CPUs
-        // And there can be many so instead of templatizing
-        // we might consider another appraoch
-        if (q_seq_len >= 768) {
-          cpu_flash_attention<CTYPE, 256, 512>(
-              output,
-              q_projected,
-              sliced_key_cache,
-              sliced_value_cache,
-              dropout_p,
-              is_causal,
-              attn_mask,
-              scale,
-              true,
-              start_pos);
-        } else if (q_seq_len >= 192) {
-          cpu_flash_attention<CTYPE, 64, 512>(
-              output,
-              q_projected,
-              sliced_key_cache,
-              sliced_value_cache,
-              dropout_p,
-              is_causal,
-              attn_mask,
-              scale,
-              true,
-              start_pos);
-        } else {
-          cpu_flash_attention<CTYPE, 32, 512>(
-              output,
-              q_projected,
-              sliced_key_cache,
-              sliced_value_cache,
-              dropout_p,
-              is_causal,
-              attn_mask,
-              scale,
-              true,
-              start_pos);
-        }
-      });
   return output;
 }
 } // namespace native
