add support on habana platform (#131)

* add habana

* add mask

* fix mask in outer_product_mean

* add dap

* add hmp

* merge training code

* add chunk for inference

* fix extra-msa stack for training

* support ddp in training

* fix inference bugs

* code refactoring for habana

* support hmp training

* enable all inference and train on Gaudi/Gaudi2 with optimized perf with latest base (#139)

* enable all inference and train on Gaudi/Gaudi2 with optimized perf

* refine code to adapt new base

* refine code to fix issues in code review
Co-authored-by: habanachina <habanachina@habana.ai>
Co-authored-by: Leo Zhao <48052473+LeoZhao-Habana@users.noreply.github.com>
Co-authored-by: habanachina <habanachina@habana.ai>

fastfold/habana/inject_habana.py

+# Copyright 2022 BioMap (Beijing) Intelligence Technology Limited
+# Copyright 2022 HPC-AI Technology Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+
+from fastfold.habana.fastnn import EvoformerStack, ExtraMSAStack
+
+#from fastfold.model.fastnn.embedders import TemplateEmbedder
+#from fastfold.model.fastnn.embedders_multimer import TemplateEmbedderMultimer
+#from fastfold.model.fastnn.ops import RecyclingEmbedder, InputEmbedder
+
+
+def copy_layernorm(model_fast, model_ori):
+    model_fast.weight.copy_(model_ori.weight)
+    model_fast.bias.copy_(model_ori.bias)
+
+
+def copy_linear(model_fast, model_ori):
+    model_fast.weight.copy_(model_ori.weight)
+    if model_fast.use_bias:
+        model_fast.bias.copy_(model_ori.bias)
+
+
+def copy_native_linear(model_fast, model_ori):
+    model_fast.weight.copy_(model_ori.weight)
+    try:
+        model_fast.bias.copy_(model_ori.bias)
+    except:
+        pass
+
+
+def copy_kv_linear(model_fast, ori_k, ori_v):
+    model_fast.weight.copy_(torch.cat((ori_k.weight, ori_v.weight), dim=0))
+
+
+def copy_qkv_linear(model_fast, ori_q, ori_k, ori_v):
+    model_fast.weight.copy_(torch.cat((ori_q.weight, ori_k.weight, ori_v.weight), dim=0))
+
+
+def copy_attention(model_fast, model_ori):
+    copy_qkv_linear(model_fast.to_qkv, model_ori.linear_q, model_ori.linear_k, model_ori.linear_v)
+    copy_linear(model_fast.gating_linear, model_ori.linear_g)
+    copy_linear(model_fast.o_linear, model_ori.linear_o)
+
+    try:
+        model_fast.gating_bias.copy_(model_ori.linear_g.bias)
+    except:
+        print("no gating_bias need copy")
+
+
+def copy_left_right(model_fast, ori_left, ori_right):
+    model_fast.weight.copy_(torch.cat((ori_left.weight, ori_right.weight), dim=0))
+    model_fast.bias.copy_(torch.cat((ori_left.bias, ori_right.bias), dim=0))
+
+
+def copy_transition(model_fast, model_ori):
+    copy_layernorm(model_fast.norm, model_ori.layer_norm)
+    copy_linear(model_fast.linear1, model_ori.linear_1)
+    copy_linear(model_fast.linear2, model_ori.linear_2)
+
+
+def copy_triangle(model_fast, model_ori):
+    copy_layernorm(model_fast.layernorm1, model_ori.layer_norm_in)
+    copy_layernorm(model_fast.layernorm2, model_ori.layer_norm_out)
+    copy_linear(model_fast.output_gate, model_ori.linear_g)
+    copy_linear(model_fast.output_projection, model_ori.linear_z)
+    model_fast.output_bias.copy_(model_ori.linear_z.bias)
+
+    copy_linear(model_fast.left_projection, model_ori.linear_a_p)
+    copy_linear(model_fast.right_projection, model_ori.linear_b_p)
+
+    copy_linear(model_fast.left_gate, model_ori.linear_a_g)
+    copy_linear(model_fast.right_gate, model_ori.linear_b_g)
+
+
+def copy_triangle_att(model_fast, model_ori):
+    copy_layernorm(model_fast.layernorm1, model_ori.layer_norm)
+    model_fast.linear_b_weights = model_ori.linear.weight
+    copy_attention(model_fast.attention, model_ori.mha)
+
+    model_fast.out_bias.copy_(model_ori.mha.linear_o.bias)
+
+
+def copy_native_att(model_fast, model_ori):
+    copy_native_linear(model_fast.linear_q, model_ori.linear_q)
+    copy_native_linear(model_fast.linear_k, model_ori.linear_k)
+    copy_native_linear(model_fast.linear_v, model_ori.linear_v)
+    copy_native_linear(model_fast.linear_o, model_ori.linear_o)
+    if model_ori.gating:
+        copy_native_linear(model_fast.linear_g, model_ori.linear_g)
+
+
+def copy_evoformer_para(block_fast, block_ori):
+    # msa_stack
+    # MSARowAttentionWithPairBias
+    copy_layernorm(block_fast.msa.MSARowAttentionWithPairBias.layernormM,
+                   block_ori.msa_att_row.layer_norm_m)
+    copy_layernorm(block_fast.msa.MSARowAttentionWithPairBias.layernormZ,
+                   block_ori.msa_att_row.layer_norm_z)
+
+    copy_attention(block_fast.msa.MSARowAttentionWithPairBias.attention, block_ori.msa_att_row.mha)
+
+    block_fast.msa.MSARowAttentionWithPairBias.linear_b_weights.copy_(
+        block_ori.msa_att_row.linear_z.weight)
+
+    block_fast.msa.MSARowAttentionWithPairBias.out_bias.copy_(
+        block_ori.msa_att_row.mha.linear_o.bias)
+
+    # MSAColumnAttention
+    copy_layernorm(block_fast.msa.MSAColumnAttention.layernormM,
+                   block_ori.msa_att_col._msa_att.layer_norm_m)
+
+    copy_attention(block_fast.msa.MSAColumnAttention.attention, block_ori.msa_att_col._msa_att.mha)
+
+    # MSATransition
+    copy_transition(block_fast.msa.MSATransition, block_ori.core.msa_transition)
+
+    # communication
+    copy_layernorm(block_fast.communication.layernormM,
+                   block_ori.core.outer_product_mean.layer_norm)
+    copy_linear(block_fast.communication.linear_a, block_ori.core.outer_product_mean.linear_1)
+    copy_linear(block_fast.communication.linear_b, block_ori.core.outer_product_mean.linear_2)
+    copy_linear(block_fast.communication.o_linear, block_ori.core.outer_product_mean.linear_out)
+
+    # pair_stack
+    # TriangleMultiplicationOutgoing
+    copy_triangle(block_fast.pair.TriangleMultiplicationOutgoing, block_ori.core.tri_mul_out)
+    # TriangleMultiplicationIncoming
+    copy_triangle(block_fast.pair.TriangleMultiplicationIncoming, block_ori.core.tri_mul_in)
+
+    # TriangleAttentionStartingNode
+    copy_triangle_att(block_fast.pair.TriangleAttentionStartingNode, block_ori.core.tri_att_start)
+    copy_triangle_att(block_fast.pair.TriangleAttentionEndingNode, block_ori.core.tri_att_end)
+
+    copy_transition(block_fast.pair.PairTransition, block_ori.core.pair_transition)
+
+
+def copy_global_attention(model_fast, model_ori):
+    copy_linear(model_fast.to_q, model_ori.linear_q)
+    copy_kv_linear(model_fast.to_kv, model_ori.linear_k, model_ori.linear_v)
+    copy_linear(model_fast.gating_linear, model_ori.linear_g)
+    copy_linear(model_fast.o_linear, model_ori.linear_o)
+
+    try:
+        model_fast.gating_bias.copy_(model_ori.linear_g.bias)
+    except:
+        print("no gating_bias need copy")
+
+
+def copy_extra_msa_para(block_fast, block_ori):
+    # msa_stack
+    # MSARowAttentionWithPairBias
+    copy_layernorm(
+        block_fast.msa_stack.MSARowAttentionWithPairBias.layernormM,
+        block_ori.msa_att_row.layer_norm_m,
+    )
+    copy_layernorm(
+        block_fast.msa_stack.MSARowAttentionWithPairBias.layernormZ,
+        block_ori.msa_att_row.layer_norm_z,
+    )
+
+    copy_attention(
+        block_fast.msa_stack.MSARowAttentionWithPairBias.attention,
+        block_ori.msa_att_row.mha,
+    )
+
+    block_fast.msa_stack.MSARowAttentionWithPairBias.linear_b_weights.copy_(
+        block_ori.msa_att_row.linear_z.weight)
+
+    block_fast.msa_stack.MSARowAttentionWithPairBias.out_bias.copy_(
+        block_ori.msa_att_row.mha.linear_o.bias)
+
+    # MSAColumnAttention
+    copy_layernorm(
+        block_fast.msa_stack.MSAColumnAttention.layernormM,
+        block_ori.msa_att_col.layer_norm_m,
+    )
+
+    copy_global_attention(
+        block_fast.msa_stack.MSAColumnAttention.global_attention,
+        block_ori.msa_att_col.global_attention,
+    )
+
+    # MSATransition
+    copy_transition(block_fast.msa_stack.MSATransition, block_ori.core.msa_transition)
+
+    # communication
+    comm_model = (
+        block_ori.core.
+        outer_product_mean  # if not block_ori.is_multimer else block_ori.outer_product_mean
+    )
+    copy_layernorm(block_fast.communication.layernormM, comm_model.layer_norm)
+    copy_linear(block_fast.communication.linear_a, comm_model.linear_1)
+    copy_linear(block_fast.communication.linear_b, comm_model.linear_2)
+    copy_linear(block_fast.communication.o_linear, comm_model.linear_out)
+
+    # pair_stack
+    # TriangleMultiplicationOutgoing
+    copy_triangle(block_fast.pair_stack.TriangleMultiplicationOutgoing, block_ori.core.tri_mul_out)
+    # TriangleMultiplicationIncoming
+    copy_triangle(block_fast.pair_stack.TriangleMultiplicationIncoming, block_ori.core.tri_mul_in)
+
+    # TriangleAttentionStartingNode
+    copy_triangle_att(
+        block_fast.pair_stack.TriangleAttentionStartingNode,
+        block_ori.core.tri_att_start,
+    )
+    copy_triangle_att(block_fast.pair_stack.TriangleAttentionEndingNode, block_ori.core.tri_att_end)
+
+    copy_transition(block_fast.pair_stack.PairTransition, block_ori.core.pair_transition)
+
+
+def copy_template_pair_stack_para(block_fast, block_ori):
+    # TriangleMultiplicationOutgoing
+    copy_triangle(block_fast.TriangleMultiplicationOutgoing, block_ori.tri_mul_out)
+    # TriangleMultiplicationIncoming
+    copy_triangle(block_fast.TriangleMultiplicationIncoming, block_ori.tri_mul_in)
+
+    # TriangleAttentionStartingNode
+    copy_triangle_att(block_fast.TriangleAttentionStartingNode, block_ori.tri_att_start)
+    copy_triangle_att(block_fast.TriangleAttentionEndingNode, block_ori.tri_att_end)
+
+    copy_transition(block_fast.PairTransition, block_ori.pair_transition)
+
+
+def copy_template_pair_block_para(fast_module, target_module):
+    with torch.no_grad():
+        for ori_block, fast_block in zip(target_module.blocks, fast_module.blocks):
+            copy_template_pair_stack_para(fast_block, ori_block)
+            if ori_block.training == False:
+                fast_block.eval()
+
+
+def copy_template_para(block_fast, block_ori):
+    # TemplateAngleEmbedder
+    copy_linear(block_fast.template_angle_embedder.linear_1,
+                block_ori.template_angle_embedder.linear_1)
+    copy_linear(block_fast.template_angle_embedder.linear_2,
+                block_ori.template_angle_embedder.linear_2)
+
+    # TemplatePairEmbedder
+    copy_linear(block_fast.template_pair_embedder.linear, block_ori.template_pair_embedder.linear)
+
+    # TemplatePairStack
+    copy_template_pair_block_para(block_fast.template_pair_stack, block_ori.template_pair_stack)
+    copy_layernorm(block_fast.template_pair_stack.layer_norm,
+                   block_ori.template_pair_stack.layer_norm)
+
+    # TemplatePointwiseAttention
+    copy_native_att(block_fast.template_pointwise_att.mha, block_ori.template_pointwise_att.mha)
+
+
+def copy_template_multimer_para(block_fast, block_ori):
+    # TemplatePairEmbedderMultimer
+    copy_linear(block_fast.template_pair_embedder.dgram_linear,
+                block_ori.template_pair_embedder.dgram_linear)
+    copy_linear(block_fast.template_pair_embedder.aatype_linear_1,
+                block_ori.template_pair_embedder.aatype_linear_1)
+    copy_linear(block_fast.template_pair_embedder.aatype_linear_2,
+                block_ori.template_pair_embedder.aatype_linear_2)
+    copy_layernorm(block_fast.template_pair_embedder.query_embedding_layer_norm,
+                   block_ori.template_pair_embedder.query_embedding_layer_norm)
+    copy_linear(block_fast.template_pair_embedder.query_embedding_linear,
+                block_ori.template_pair_embedder.query_embedding_linear)
+    copy_linear(block_fast.template_pair_embedder.pseudo_beta_mask_linear,
+                block_ori.template_pair_embedder.pseudo_beta_mask_linear)
+    copy_linear(block_fast.template_pair_embedder.x_linear,
+                block_ori.template_pair_embedder.x_linear)
+    copy_linear(block_fast.template_pair_embedder.y_linear,
+                block_ori.template_pair_embedder.y_linear)
+    copy_linear(block_fast.template_pair_embedder.z_linear,
+                block_ori.template_pair_embedder.z_linear)
+    copy_linear(block_fast.template_pair_embedder.backbone_mask_linear,
+                block_ori.template_pair_embedder.backbone_mask_linear)
+
+    # TemplateSingleEmbedderMultimer
+    copy_linear(block_fast.template_single_embedder.template_single_embedder,
+                block_ori.template_single_embedder.template_single_embedder)
+    copy_linear(block_fast.template_single_embedder.template_projector,
+                block_ori.template_single_embedder.template_projector)
+
+    # TemplatePairStack
+    copy_template_pair_block_para(block_fast.template_pair_stack, block_ori.template_pair_stack)
+    copy_layernorm(block_fast.template_pair_stack.layer_norm,
+                   block_ori.template_pair_stack.layer_norm)
+
+    # linear_t
+    copy_linear(block_fast.linear_t, block_ori.linear_t)
+
+
+def inject_evoformer(model):
+    with torch.no_grad():
+        target_module = model.evoformer
+        fast_module = EvoformerStack(
+            c_m=target_module.blocks[0].msa_att_row.c_in,
+            c_z=target_module.blocks[0].msa_att_row.c_z,
+            c_s=target_module.linear.out_features,
+            no_blocks=len(target_module.blocks),
+            blocks_per_ckpt=target_module.blocks_per_ckpt,
+            clear_cache_between_blocks=target_module.clear_cache_between_blocks,
+            is_multimer=target_module.blocks[0].is_multimer,
+        )
+        for target_block, fast_block in zip(target_module.blocks, fast_module.blocks):
+            copy_evoformer_para(fast_block, target_block)
+        if target_module.training == False:
+            fast_module.eval()
+        copy_linear(fast_module.linear, target_module.linear)
+        model.evoformer = fast_module
+
+
+def inject_extramsa(model):
+    with torch.no_grad():
+        target_module = model.extra_msa_stack
+        fast_module = ExtraMSAStack(
+            c_m=target_module.blocks[0].msa_att_row.c_in,
+            c_z=target_module.blocks[0].msa_att_row.c_z,
+            no_blocks=len(target_module.blocks),
+            blocks_per_ckpt=1,
+            clear_cache_between_blocks=target_module.clear_cache_between_blocks,
+            is_multimer=target_module.blocks[0].is_multimer,
+        )
+        for target_block, fast_block in zip(target_module.blocks, fast_module.blocks):
+            copy_extra_msa_para(fast_block, target_block)
+        if target_module.training == False:
+            fast_module.eval()
+        model.extra_msa_stack = fast_module
+
+
+def inject_template(model):
+    with torch.no_grad():
+        if model.evoformer.blocks[0].is_multimer:
+            target_module = model.template_embedder
+            fast_module = TemplateEmbedderMultimer(config=model.template_embedder.config)
+            copy_template_multimer_para(fast_module, target_module)
+            if target_module.training == False:
+                fast_module.eval()
+            model.template_embedder = fast_module
+        else:
+            target_module = model.template_embedder
+            fast_module = TemplateEmbedder(config=model.template_embedder.config)
+            copy_template_para(fast_module, target_module)
+            if target_module.training == False:
+                fast_module.eval()
+            model.template_embedder = fast_module
+
+
+def inject_embedder(model):
+    if model.evoformer.blocks[0].is_multimer:
+        return
+
+    # recycle embedder
+    with torch.no_grad():
+        target_module = model.recycling_embedder
+        fast_module = RecyclingEmbedder(c_m=target_module.c_m,
+                                        c_z=target_module.c_z,
+                                        min_bin=target_module.min_bin,
+                                        max_bin=target_module.max_bin,
+                                        no_bins=target_module.no_bins,
+                                        inf=target_module.inf)
+        copy_native_linear(fast_module.linear, target_module.linear)
+        copy_layernorm(fast_module.layer_norm_m, target_module.layer_norm_m)
+        copy_layernorm(fast_module.layer_norm_z, target_module.layer_norm_z)
+        if target_module.training == False:
+            fast_module.eval()
+        model.recycling_embedder = fast_module
+
+    # input embedder
+    with torch.no_grad():
+        target_module = model.input_embedder
+        fast_module = InputEmbedder(
+            tf_dim=target_module.tf_dim,
+            msa_dim=target_module.msa_dim,
+            c_z=target_module.c_z,
+            c_m=target_module.c_m,
+            relpos_k=target_module.relpos_k,
+        )
+        copy_linear(fast_module.linear_tf_z_i, target_module.linear_tf_z_i)
+        copy_linear(fast_module.linear_tf_z_j, target_module.linear_tf_z_j)
+        copy_linear(fast_module.linear_tf_m, target_module.linear_tf_m)
+        copy_linear(fast_module.linear_msa_m, target_module.linear_msa_m)
+        copy_linear(fast_module.linear_relpos, target_module.linear_relpos)
+        if target_module.training == False:
+            fast_module.eval()
+        model.input_embedder = fast_module
+
+
+def inject_habana(model):
+    inject_evoformer(model)
+    inject_extramsa(model)
+    #inject_template(model)
+    #inject_embedder(model)
+    return model

fastfold/model/hub/alphafold.py

@@ -41,6 +41,7 @@ from fastfold.utils.tensor_utils import (
     tensor_tree_map,
 )
 
+import fastfold.habana as habana
 
 class AlphaFold(nn.Module):
     """
@@ -173,6 +174,9 @@ class AlphaFold(nn.Module):
         # Primary output dictionary
         outputs = {}
 
+        if habana.is_habana():
+            from habana.hpuhelper import hpu_perf
+            perf = hpu_perf("iteration", sync=False)
         dtype = next(self.parameters()).dtype
         for k in feats:
             if(feats[k].dtype == torch.float32):
@@ -190,7 +194,8 @@ class AlphaFold(nn.Module):
         pair_mask = seq_mask[..., None] * seq_mask[..., None, :]
         msa_mask = feats["msa_mask"]
 
-        # Initialize the MSA and pair representations
+        if habana.is_habana():
+            perf.checkahead("1: Initialize the MSA and pair representations")
 
         # m: [*, S_c, N, C_m]
         # z: [*, N, N, C_z]
@@ -252,7 +257,8 @@ class AlphaFold(nn.Module):
         # Possibly prevents memory fragmentation
         del m_1_prev, z_prev, x_prev
 
-        # Embed the templates + merge with MSA/pair embeddings
+        if habana.is_habana():
+            perf.checkahead("2: Embed the templates + merge with MSA/pair embeddings")
         if self.config.template.enabled:
             template_feats = {
                 k: v for k, v in feats.items() if k.startswith("template_")
@@ -320,7 +326,8 @@ class AlphaFold(nn.Module):
                     )
             del template_feats, template_embeds
 
-        # Embed extra MSA features + merge with pairwise embeddings
+        if habana.is_habana():
+            perf.checkahead("3: Embed extra MSA features + merge with pairwise embeddings")
         if self.config.extra_msa.enabled:
             if(self.globals.is_multimer):
                 extra_msa_fn = data_transforms_multimer.build_extra_msa_feat
@@ -354,7 +361,8 @@ class AlphaFold(nn.Module):
                 )[0]
             del extra_msa_feat, extra_msa_fn
 
-        # Run MSA + pair embeddings through the trunk of the network
+        if habana.is_habana():
+            perf.checkahead("4: Run MSA + pair embeddings through the trunk of the network")
         # m: [*, S, N, C_m]
         # z: [*, N, N, C_z]
         # s: [*, N, C_s]
@@ -385,7 +393,8 @@ class AlphaFold(nn.Module):
         outputs["pair"] = z
         outputs["single"] = s
 
-        # Predict 3D structure
+        if habana.is_habana():
+            perf.checkahead("5: Predict 3D structure")
         outputs["sm"] = self.structure_module(
             s,
             z,
@@ -409,6 +418,9 @@ class AlphaFold(nn.Module):
         # [*, N, 3]
         x_prev = outputs["final_atom_positions"]
 
+        if habana.is_habana():
+            perf.checkahead("6: stop iteration")
+
         return outputs, m_1_prev, z_prev, x_prev
 
     def _disable_activation_checkpointing(self):
@@ -490,6 +502,9 @@ class AlphaFold(nn.Module):
         # Main recycling loop
         num_iters = batch["aatype"].shape[-1]
         for cycle_no in range(num_iters):
+            if habana.is_habana():
+                from habana.hpuhelper import hpu_perf
+                perf = hpu_perf(f"cycle {cycle_no+1}/{num_iters}")
             # Select the features for the current recycling cycle
             fetch_cur_batch = lambda t: t[..., cycle_no]
             feats = tensor_tree_map(fetch_cur_batch, batch)
@@ -511,7 +526,8 @@ class AlphaFold(nn.Module):
                     x_prev,
                     _recycle=(num_iters > 1)
                 )
-
+            if habana.is_habana():
+                perf.checknow("cycle finish")
         # Run auxiliary heads
         outputs.update(self.aux_heads(outputs))
 

fastfold/model/hub/loss.py

@@ -22,6 +22,7 @@ import torch.nn as nn
 from torch.distributions.bernoulli import Bernoulli
 from typing import Dict, Optional, Tuple
 
+import fastfold.habana as habana
 from fastfold.common import residue_constants
 from fastfold.utils import feats
 from fastfold.utils.rigid_utils import Rotation, Rigid
@@ -486,8 +487,12 @@ def lddt_loss(
 
     score = score.detach()
 
-    bin_index = torch.floor(score * no_bins).long()
-    bin_index = torch.clamp(bin_index, max=(no_bins - 1))
+    if habana.is_habana():
+        bin_index = torch.floor(score * no_bins)
+        bin_index = torch.clamp(bin_index, max=(no_bins - 1)).float().long()
+    else:
+        bin_index = torch.floor(score * no_bins).long()
+        bin_index = torch.clamp(bin_index, max=(no_bins - 1))
     lddt_ca_one_hot = torch.nn.functional.one_hot(
         bin_index, num_classes=no_bins
     )
@@ -931,16 +936,23 @@ def between_residue_clash_loss(
     )
 
     # Backbone C--N bond between subsequent residues is no clash.
-    c_one_hot = torch.nn.functional.one_hot(
-        residue_index.new_tensor(2), num_classes=14
-    )
+    if habana.is_habana():
+        c_one_hot = torch.tensor([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], device=residue_index.device)
+    else:
+        c_one_hot = torch.nn.functional.one_hot(
+            residue_index.new_tensor(2), num_classes=14
+        )
     c_one_hot = c_one_hot.reshape(
         *((1,) * len(residue_index.shape[:-1])), *c_one_hot.shape
     )
     c_one_hot = c_one_hot.type(fp_type)
-    n_one_hot = torch.nn.functional.one_hot(
-        residue_index.new_tensor(0), num_classes=14
-    )
+
+    if habana.is_habana():
+        n_one_hot = torch.tensor([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], device=residue_index.device)
+    else:
+        n_one_hot = torch.nn.functional.one_hot(
+            residue_index.new_tensor(0), num_classes=14
+        )
     n_one_hot = n_one_hot.reshape(
         *((1,) * len(residue_index.shape[:-1])), *n_one_hot.shape
     )
@@ -963,7 +975,11 @@ def between_residue_clash_loss(
     cys_sg_idx = cys_sg_idx.reshape(
         *((1,) * len(residue_index.shape[:-1])), 1
     ).squeeze(-1)
-    cys_sg_one_hot = torch.nn.functional.one_hot(cys_sg_idx, num_classes=14)
+    
+    if habana.is_habana():
+        cys_sg_one_hot = torch.tensor([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], device=n_one_hot.device)
+    else:
+        cys_sg_one_hot = torch.nn.functional.one_hot(cys_sg_idx, num_classes=14)
     disulfide_bonds = (
         cys_sg_one_hot[..., None, None, :, None]
         * cys_sg_one_hot[..., None, None, None, :]
@@ -1596,12 +1612,16 @@ class AlphaFoldLoss(nn.Module):
                 out["sm"]["unnormalized_angles"],
                 **{**batch, **self.config.supervised_chi},
             ),
+            # Habana: TODO comment out below part to WA error in HMP
             "violation": lambda: violation_loss(
                 out["violation"],
                 **batch,
             ),
         }
 
+        if habana.is_habana():
+            del loss_fns["violation"]
+
         if(self.config.tm.enabled):
             loss_fns["tm"] = lambda: tm_loss(
                 logits=out["tm_logits"],
@@ -1632,4 +1652,4 @@ class AlphaFoldLoss(nn.Module):
         if(not _return_breakdown):
             return cum_loss
         
-        return cum_loss, losses
\ No newline at end of file
+        return cum_loss, losses

fastfold/model/nn/embedders.py

@@ -360,7 +360,7 @@ class TemplateAngleEmbedder(nn.Module):
         Returns:
             x: [*, N_templ, N_res, C_out] embedding
         """
-        x = self.linear_1(x)
+        x = self.linear_1(x.to(dtype=self.linear_1.weight.dtype))
         x = self.relu(x)
         x = self.linear_2(x)
 
@@ -446,6 +446,6 @@ class ExtraMSAEmbedder(nn.Module):
         Returns:
             [*, N_extra_seq, N_res, C_out] embedding
         """
-        x = self.linear(x)
+        x = self.linear(x.to(dtype=self.linear.weight.dtype))
 
-        return x
\ No newline at end of file
+        return x

fastfold/model/nn/primitives.py

@@ -29,6 +29,7 @@ from fastfold.utils.tensor_utils import (
     _chunk_slice,
 )
 
+import fastfold.habana as habana
 
 def _prod(nums):
     out = 1
@@ -214,13 +215,20 @@ def _attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor,
 
     # [*, H, Q, K]
     a = torch.matmul(query, key)
+    if habana.is_habana():
+        from fastfold.habana.fastnn.custom_op import fused_softmax, fused_softmax_bias
+        if len(biases) == 1:
+            a = fused_softmax(a, biases[0], -1)
+        else:
+            a = fused_softmax_bias(a, biases[0], biases[1], -1)
+    else:
+        for b in biases:
+            a += b
 
-    for b in biases:
-        a += b
-
-    a = softmax(a, -1)
+        a = softmax(a, -1)
 
     # [*, H, Q, C_hidden]
+    a = a.to(dtype=value.dtype)
     a = torch.matmul(a, value)
 
     # [*, Q, H, C_hidden]
@@ -463,10 +471,15 @@ class GlobalAttention(nn.Module):
             k.transpose(-1, -2),  # [*, N_res, C_hidden, N_seq]
         )
         bias = (self.inf * (mask - 1))[..., :, None, :]
-        a += bias
-        a = softmax(a)
+        if habana.is_habana():
+            from fastfold.habana.fastnn.custom_op import fused_softmax, fused_softmax_bias
+            a = fused_softmax(a, bias, -1)
+        else:
+            a += bias
+            a = softmax(a)
 
         # [*, N_res, H, C_hidden]
+        a = a.to(dtype=v.dtype)
         o = torch.matmul(
             a,
             v,

fastfold/model/nn/structure_module.py

@@ -39,6 +39,7 @@ from fastfold.utils.tensor_utils import (
     flatten_final_dims,
 )
 
+import fastfold.habana as habana
 
 class AngleResnetBlock(nn.Module):
     def __init__(self, c_hidden):
@@ -397,10 +398,20 @@ class InvariantPointAttention(nn.Module):
             pt_att = sum([c**2 for c in pt_att])
         else:
             # [*, N_res, N_res, H, P_q, 3]
-            pt_att = q_pts.unsqueeze(-4) - k_pts.unsqueeze(-5)
-            pt_att = pt_att**2
-            # [*, N_res, N_res, H, P_q]
-            pt_att = sum(torch.unbind(pt_att, dim=-1))
+            ######################################
+            q_pts_t0 = q_pts.unsqueeze(-4)
+            q_shape = q_pts_t0.shape
+            q_pts_t0 = q_pts_t0.reshape([q_shape[0], q_shape[1], -1])
+            k_pts_t0 = k_pts.unsqueeze(-5)
+            k_shape = k_pts_t0.shape
+            k_pts_t0 = k_pts_t0.reshape([k_shape[0], k_shape[1], -1])
+            q_k = q_pts_t0 - k_pts_t0
+            q_k = q_k ** 2
+            q_k_shape = q_k.shape
+            pt_att = q_k.reshape(q_k_shape[:2] + q_shape[-3:])
+            #####################################
+            pt_att = pt_att.permute(0, 4, 1, 2, 3)
+            pt_att = torch.sum(pt_att, 1)
 
         head_weights = self.softplus(self.head_weights).view(
             *((1,) * len(pt_att.shape[:-2]) + (-1, 1))
@@ -408,7 +419,12 @@ class InvariantPointAttention(nn.Module):
         head_weights = head_weights * math.sqrt(
             1.0 / (3 * (self.no_qk_points * 9.0 / 2))
         )
-        pt_att = pt_att * head_weights
+        ##############################
+        pt_att_t0 = pt_att.permute(0, 3, 1, 2)
+        head_weights_t0 = head_weights.permute(0, 3, 1, 2)
+        pt_att_o = pt_att_t0 * head_weights_t0
+        pt_att = pt_att_o.permute(0, 2,3, 1)
+        ##############################
 
         # [*, N_res, N_res, H]
         pt_att = torch.sum(pt_att, dim=-1) * (-0.5)
@@ -448,13 +464,14 @@ class InvariantPointAttention(nn.Module):
             o_pt_norm = o_pt.norm(self.eps)
         else:
             # [*, H, 3, N_res, P_v]
-            o_pt = torch.sum(
-                (
-                    a[..., None, :, :, None]
-                    * permute_final_dims(v_pts, (1, 3, 0, 2))[..., None, :, :]
-                ),
-                dim=-2,
-            )
+            ###################################
+            a1 = a[..., None, :, :, None]
+            a1 = a1.permute(0, 1, 2, 4, 3)
+            b = permute_final_dims(v_pts, (1, 3, 0, 2))[..., None, :, :]
+            b = b.permute(0, 1, 2, 4, 3)
+            c = a1 * b
+            o_pt = torch.sum(c, -1)
+            ###################################
 
             # [*, N_res, H, P_v, 3]
             o_pt = permute_final_dims(o_pt, (2, 0, 3, 1))
@@ -788,6 +805,10 @@ class StructureModule(nn.Module):
             if i < (self.no_blocks - 1):
                 rigids = rigids.stop_rot_gradient()
 
+            if habana.is_habana():
+                import habana_frameworks.torch.core as htcore
+                htcore.mark_step()
+
         outputs = dict_multimap(torch.stack, outputs)
         outputs["single"] = s
 

fastfold/model/nn/template.py

@@ -40,6 +40,7 @@ from fastfold.utils.tensor_utils import (
     flatten_final_dims,
 )
 
+import fastfold.habana as habana
 
 class TemplatePointwiseAttention(nn.Module):
     """
@@ -121,10 +122,13 @@ class TemplatePointwiseAttention(nn.Module):
 
         # [*, N_res, N_res, 1, C_z]
         biases = [bias]
-        if chunk_size is not None:
-            z = self._chunk(z, t, biases, chunk_size)
-        else:
+        if habana.is_habana():
             z = self.mha(q_x=z, kv_x=t, biases=biases)
+        else:
+            if chunk_size is not None:
+                z = self._chunk(z, t, biases, chunk_size)
+            else:
+                z = self.mha(q_x=z, kv_x=t, biases=biases)
 
         # [*, N_res, N_res, C_z]
         z = z.squeeze(-2)

fastfold/utils/feats.py

@@ -44,7 +44,7 @@ def dgram_from_positions(
     )
     lower = torch.linspace(min_bin, max_bin, no_bins, device=pos.device) ** 2
     upper = torch.cat([lower[1:], lower.new_tensor([inf])], dim=-1)
-    dgram = ((dgram > lower) * (dgram < upper)).type(dgram.dtype)
+    dgram = ((dgram > lower).type(dgram.dtype) * (dgram < upper)).type(dgram.dtype)
 
     return dgram
 
@@ -91,7 +91,7 @@ def build_template_angle_feat(template_feats: Dict[str, Any]) -> torch.Tensor:
     torsion_angles_mask = template_feats["template_torsion_angles_mask"]
     template_angle_feat = torch.cat(
         [
-            nn.functional.one_hot(template_aatype, 22),
+            nn.functional.one_hot(template_aatype, 22).to(torch.float32),
             torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2], 14),
             alt_torsion_angles_sin_cos.reshape(
                 *alt_torsion_angles_sin_cos.shape[:-2], 14
@@ -136,10 +136,10 @@ def build_template_pair_feat(
     to_concat.append(
         aatype_one_hot[..., None, :, :].expand(
             *aatype_one_hot.shape[:-2], n_res, -1, -1
-        )
+        ).to(dgram.dtype)
     )
     to_concat.append(
-        aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2], -1, n_res, -1)
+        aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2], -1, n_res, -1).to(dgram.dtype)
     )
 
     n, ca, c = [rc.atom_order[a] for a in ["N", "CA", "C"]]
@@ -179,7 +179,7 @@ def build_template_pair_feat(
 def build_extra_msa_feat(batch: Dict[str, Any]) -> torch.Tensor:
     msa_1hot = nn.functional.one_hot(batch["extra_msa"], 23)
     msa_feat = [
-        msa_1hot,
+        msa_1hot.to(torch.float32),
         batch["extra_has_deletion"].unsqueeze(-1),
         batch["extra_deletion_value"].unsqueeze(-1),
     ]

fastfold/utils/tensor_utils.py

@@ -19,6 +19,7 @@ import torch
 import torch.nn as nn
 from typing import Tuple, List, Callable, Any, Dict, Sequence, Optional
 
+import fastfold.habana as habana
 
 def permute_final_dims(tensor: torch.Tensor, inds: List[int]):
     zero_index = -1 * len(inds)
@@ -407,4 +408,8 @@ def chunk_layer(
     reshape = lambda t: t.view(orig_batch_dims + t.shape[1:])
     out = tensor_tree_map(reshape, out)
 
+    if habana.is_habana():
+        import habana_frameworks.torch.core as htcore
+        htcore.mark_step()
+
     return out

habana/hpuhelper.py

+import time
+import habana_frameworks.torch as ht
+
+class hpu_perf:
+    def __init__(self, module, log=True, mark_step=True, memoryinfo=False, sync=False):
+        if log:
+            print(f" {module}: start")
+        self.module = module
+        self.stime = time.perf_counter()
+        self.mark = mark_step
+        self.mem = memoryinfo
+        self.sync = sync
+        self.log = log
+        if self.mem:
+            ht.hpu.reset_peak_memory_stats()
+        self.prelog = None
+
+    def checknow(self, log):
+        if self.mark:
+            ht.core.mark_step()
+            if self.sync:
+                ht.core.hpu.default_stream().synchronize()
+        if self.mem:
+            print(ht.hpu.memory_summary())
+
+        tmp = time.perf_counter()
+        if self.log:
+            print(" {}: {} takes {:.2f} ms".format(self.module, log, (tmp - self.stime)*1000))
+        self.stime = tmp
+
+    def checkahead(self, log):
+        if self.mark:
+            ht.core.mark_step()
+            if self.sync:
+                ht.core.hpu.default_stream().synchronize()
+        if self.mem:
+            print(ht.hpu.memory_summary())
+
+        tmp = time.perf_counter()
+        if self.prelog is not None and self.log:
+            print(" {}: {} takes {:.2f} ms".format(self.module, self.prelog, (tmp - self.stime)*1000))
+        self.stime = tmp
+        self.prelog = log

habana/inference.py

+# Copyright 2021 AlQuraishi Laboratory
+# Copyright 2021 DeepMind Technologies Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import contextlib
+import os
+import pickle
+import random
+import shutil
+import sys
+import tempfile
+import time
+from datetime import date
+
+import numpy as np
+import torch
+import torch.multiprocessing as mp
+
+import habana_frameworks.torch.core as htcore
+
+import fastfold.habana as habana
+import fastfold.relax.relax as relax
+from fastfold.common import protein, residue_constants
+from fastfold.config import model_config
+from fastfold.data import data_pipeline, feature_pipeline, templates
+from fastfold.data.parsers import parse_fasta
+from fastfold.habana.distributed import init_dist
+from fastfold.habana.fastnn.ops import set_chunk_size
+from fastfold.habana.inject_habana import inject_habana
+from fastfold.model.hub import AlphaFold
+from fastfold.model.nn.triangular_multiplicative_update import \
+    set_fused_triangle_multiplication
+from fastfold.utils.import_weights import import_jax_weights_
+from fastfold.utils.tensor_utils import tensor_tree_map
+from fastfold.workflow.template import (FastFoldDataWorkFlow, FastFoldMultimerDataWorkFlow)
+
+
+@contextlib.contextmanager
+def temp_fasta_file(fasta_str: str):
+    with tempfile.NamedTemporaryFile('w', suffix='.fasta') as fasta_file:
+        fasta_file.write(fasta_str)
+        fasta_file.seek(0)
+        yield fasta_file.name
+
+
+def add_data_args(parser: argparse.ArgumentParser):
+    parser.add_argument(
+        '--uniref90_database_path',
+        type=str,
+        default=None,
+    )
+    parser.add_argument(
+        '--mgnify_database_path',
+        type=str,
+        default=None,
+    )
+    parser.add_argument(
+        '--pdb70_database_path',
+        type=str,
+        default=None,
+    )
+    parser.add_argument(
+        '--uniclust30_database_path',
+        type=str,
+        default=None,
+    )
+    parser.add_argument(
+        '--bfd_database_path',
+        type=str,
+        default=None,
+    )
+    parser.add_argument(
+        "--pdb_seqres_database_path",
+        type=str,
+        default=None,
+    )
+    parser.add_argument(
+        "--uniprot_database_path",
+        type=str,
+        default=None,
+    )
+    parser.add_argument('--jackhmmer_binary_path', type=str, default='/usr/bin/jackhmmer')
+    parser.add_argument('--hhblits_binary_path', type=str, default='/usr/bin/hhblits')
+    parser.add_argument('--hhsearch_binary_path', type=str, default='/usr/bin/hhsearch')
+    parser.add_argument('--kalign_binary_path', type=str, default='/usr/bin/kalign')
+    parser.add_argument("--hmmsearch_binary_path", type=str, default="hmmsearch")
+    parser.add_argument("--hmmbuild_binary_path", type=str, default="hmmbuild")
+    parser.add_argument(
+        '--max_template_date',
+        type=str,
+        default=date.today().strftime("%Y-%m-%d"),
+    )
+    parser.add_argument('--obsolete_pdbs_path', type=str, default=None)
+    parser.add_argument('--release_dates_path', type=str, default=None)
+    parser.add_argument('--chunk_size', type=int, default=None)
+    parser.add_argument('--enable_workflow',
+                        default=False,
+                        action='store_true',
+                        help='run inference with ray workflow or not')
+    parser.add_argument('--inplace', default=False, action='store_true')
+
+
+def inference_model(rank, world_size, result_q, batch, args):
+    os.environ['RANK'] = str(rank)
+    os.environ['LOCAL_RANK'] = str(rank)
+    os.environ['WORLD_SIZE'] = str(world_size)
+    # init distributed for Dynamic Axial Parallelism
+    habana.enable_habana()
+    init_dist()
+
+    device = torch.device("hpu")
+
+    config = model_config(args.model_name)
+    if args.chunk_size:
+        config.globals.chunk_size = args.chunk_size
+
+    if "v3" in args.param_path:
+        set_fused_triangle_multiplication()
+
+    config.globals.inplace = False
+    config.globals.is_multimer = args.model_preset == 'multimer'
+    model = AlphaFold(config)
+    import_jax_weights_(model, args.param_path, version=args.model_name)
+
+    model = inject_habana(model)
+    model = model.eval()
+    model = model.to(device=device)
+
+    set_chunk_size(model.globals.chunk_size)
+
+    with torch.no_grad():
+        batch = {k: torch.as_tensor(v).to(device=device) for k, v in batch.items()}
+
+        t = time.perf_counter()
+        out = model(batch)
+        htcore.mark_step()
+        print(f"Inference time: {time.perf_counter() - t}")
+
+    out = tensor_tree_map(lambda x: np.array(x.cpu()), out)
+
+    result_q.put(out)
+
+    torch.distributed.barrier()
+
+
+def main(args):
+    if args.model_preset == "multimer":
+        inference_multimer_model(args)
+    else:
+        inference_monomer_model(args)
+
+
+def inference_multimer_model(args):
+    print("running in multimer mode...")
+    config = model_config(args.model_name)
+
+    predict_max_templates = 4
+
+    template_featurizer = templates.HmmsearchHitFeaturizer(
+        mmcif_dir=args.template_mmcif_dir,
+        max_template_date=args.max_template_date,
+        max_hits=predict_max_templates,
+        kalign_binary_path=args.kalign_binary_path,
+        release_dates_path=args.release_dates_path,
+        obsolete_pdbs_path=args.obsolete_pdbs_path,
+    )
+
+    if (not args.use_precomputed_alignments):
+        if args.enable_workflow:
+            print("Running alignment with ray workflow...")
+            alignment_runner = FastFoldMultimerDataWorkFlow(
+                jackhmmer_binary_path=args.jackhmmer_binary_path,
+                hhblits_binary_path=args.hhblits_binary_path,
+                hmmsearch_binary_path=args.hmmsearch_binary_path,
+                hmmbuild_binary_path=args.hmmbuild_binary_path,
+                uniref90_database_path=args.uniref90_database_path,
+                mgnify_database_path=args.mgnify_database_path,
+                bfd_database_path=args.bfd_database_path,
+                uniclust30_database_path=args.uniclust30_database_path,
+                uniprot_database_path=args.uniprot_database_path,
+                pdb_seqres_database_path=args.pdb_seqres_database_path,
+                use_small_bfd=(args.bfd_database_path is None),
+                no_cpus=args.cpus)
+        else:
+            alignment_runner = data_pipeline.AlignmentRunnerMultimer(
+                jackhmmer_binary_path=args.jackhmmer_binary_path,
+                hhblits_binary_path=args.hhblits_binary_path,
+                hmmsearch_binary_path=args.hmmsearch_binary_path,
+                hmmbuild_binary_path=args.hmmbuild_binary_path,
+                uniref90_database_path=args.uniref90_database_path,
+                mgnify_database_path=args.mgnify_database_path,
+                bfd_database_path=args.bfd_database_path,
+                uniclust30_database_path=args.uniclust30_database_path,
+                uniprot_database_path=args.uniprot_database_path,
+                pdb_seqres_database_path=args.pdb_seqres_database_path,
+                use_small_bfd=(args.bfd_database_path is None),
+                no_cpus=args.cpus)
+    else:
+        alignment_runner = None
+
+    monomer_data_processor = data_pipeline.DataPipeline(template_featurizer=template_featurizer,)
+
+    data_processor = data_pipeline.DataPipelineMultimer(
+        monomer_data_pipeline=monomer_data_processor,)
+
+    output_dir_base = args.output_dir
+    random_seed = args.data_random_seed
+    if random_seed is None:
+        random_seed = random.randrange(sys.maxsize)
+
+    feature_processor = feature_pipeline.FeaturePipeline(config.data)
+
+    if not os.path.exists(output_dir_base):
+        os.makedirs(output_dir_base)
+    if (not args.use_precomputed_alignments):
+        alignment_dir = os.path.join(output_dir_base, "alignments")
+    else:
+        alignment_dir = args.use_precomputed_alignments
+
+    # Gather input sequences
+    fasta_path = args.fasta_path
+    with open(fasta_path, "r") as fp:
+        data = fp.read()
+
+    lines = [l.replace('\n', '') for prot in data.split('>') for l in prot.strip().split('\n', 1)
+            ][1:]
+    tags, seqs = lines[::2], lines[1::2]
+
+    for tag, seq in zip(tags, seqs):
+        local_alignment_dir = os.path.join(alignment_dir, tag)
+        if (args.use_precomputed_alignments is None):
+            if not os.path.exists(local_alignment_dir):
+                os.makedirs(local_alignment_dir)
+            else:
+                shutil.rmtree(local_alignment_dir)
+                os.makedirs(local_alignment_dir)
+
+            chain_fasta_str = f'>chain_{tag}\n{seq}\n'
+            with temp_fasta_file(chain_fasta_str) as chain_fasta_path:
+                if args.enable_workflow:
+                    print("Running alignment with ray workflow...")
+                    t = time.perf_counter()
+                    alignment_runner.run(chain_fasta_path, alignment_dir=local_alignment_dir)
+                    print(f"Alignment data workflow time: {time.perf_counter() - t}")
+                else:
+                    alignment_runner.run(chain_fasta_path, local_alignment_dir)
+
+                print(f"Finished running alignment for {tag}")
+
+    local_alignment_dir = alignment_dir
+
+    feature_dict = data_processor.process_fasta(fasta_path=fasta_path,
+                                                alignment_dir=local_alignment_dir)
+    # feature_dict = pickle.load(open("/home/lcmql/data/features_pdb1o5d.pkl", "rb"))
+
+    processed_feature_dict = feature_processor.process_features(
+        feature_dict,
+        mode='predict',
+        is_multimer=True,
+    )
+
+    batch = processed_feature_dict
+
+    manager = mp.Manager()
+    result_q = manager.Queue()
+    torch.multiprocessing.spawn(inference_model,
+                                nprocs=args.hpus,
+                                args=(args.hpus, result_q, batch, args))
+
+    out = result_q.get()
+
+    # Toss out the recycling dimensions --- we don't need them anymore
+    batch = tensor_tree_map(lambda x: np.array(x[..., -1].cpu()), batch)
+
+    plddt = out["plddt"]
+    mean_plddt = np.mean(plddt)
+
+    plddt_b_factors = np.repeat(plddt[..., None], residue_constants.atom_type_num, axis=-1)
+
+    unrelaxed_protein = protein.from_prediction(features=batch,
+                                                result=out,
+                                                b_factors=plddt_b_factors)
+
+    # Save the unrelaxed PDB.
+    unrelaxed_output_path = os.path.join(args.output_dir, f'{tag}_{args.model_name}_unrelaxed.pdb')
+    with open(unrelaxed_output_path, 'w') as f:
+        f.write(protein.to_pdb(unrelaxed_protein))
+
+    amber_relaxer = relax.AmberRelaxation(
+        use_gpu=False,
+        **config.relax,
+    )
+
+    # Relax the prediction.
+    t = time.perf_counter()
+    relaxed_pdb_str, _, _ = amber_relaxer.process(prot=unrelaxed_protein)
+    print(f"Relaxation time: {time.perf_counter() - t}")
+
+    # Save the relaxed PDB.
+    relaxed_output_path = os.path.join(args.output_dir, f'{tag}_{args.model_name}_relaxed.pdb')
+    with open(relaxed_output_path, 'w') as f:
+        f.write(relaxed_pdb_str)
+
+
+def inference_monomer_model(args):
+    print("running in monomer mode...")
+    config = model_config(args.model_name)
+
+    template_featurizer = templates.TemplateHitFeaturizer(
+        mmcif_dir=args.template_mmcif_dir,
+        max_template_date=args.max_template_date,
+        max_hits=config.data.predict.max_templates,
+        kalign_binary_path=args.kalign_binary_path,
+        release_dates_path=args.release_dates_path,
+        obsolete_pdbs_path=args.obsolete_pdbs_path)
+
+    use_small_bfd = args.preset == 'reduced_dbs'  # (args.bfd_database_path is None)
+    if use_small_bfd:
+        assert args.bfd_database_path is not None
+    else:
+        assert args.bfd_database_path is not None
+        assert args.uniclust30_database_path is not None
+
+    data_processor = data_pipeline.DataPipeline(template_featurizer=template_featurizer,)
+
+    output_dir_base = args.output_dir
+    random_seed = args.data_random_seed
+    if random_seed is None:
+        random_seed = random.randrange(sys.maxsize)
+    feature_processor = feature_pipeline.FeaturePipeline(config.data)
+    if not os.path.exists(output_dir_base):
+        os.makedirs(output_dir_base)
+    if (args.use_precomputed_alignments is None):
+        alignment_dir = os.path.join(output_dir_base, "alignments")
+    else:
+        alignment_dir = args.use_precomputed_alignments
+
+    # Gather input sequences
+    with open(args.fasta_path, "r") as fp:
+        fasta = fp.read()
+    seqs, tags = parse_fasta(fasta)
+    seq, tag = seqs[0], tags[0]
+
+    print(f"tag:{tag}\nseq[{len(seq)}]:{seq}")
+    batch = [None]
+
+    fasta_path = os.path.join(args.output_dir, "tmp.fasta")
+    with open(fasta_path, "w") as fp:
+        fp.write(f">{tag}\n{seq}")
+
+    print("Generating features...")
+    local_alignment_dir = os.path.join(alignment_dir, tag)
+
+    if (args.use_precomputed_alignments is None):
+        if not os.path.exists(local_alignment_dir):
+            os.makedirs(local_alignment_dir)
+        if args.enable_workflow:
+            print("Running alignment with ray workflow...")
+            alignment_data_workflow_runner = FastFoldDataWorkFlow(
+                jackhmmer_binary_path=args.jackhmmer_binary_path,
+                hhblits_binary_path=args.hhblits_binary_path,
+                hhsearch_binary_path=args.hhsearch_binary_path,
+                uniref90_database_path=args.uniref90_database_path,
+                mgnify_database_path=args.mgnify_database_path,
+                bfd_database_path=args.bfd_database_path,
+                uniclust30_database_path=args.uniclust30_database_path,
+                pdb70_database_path=args.pdb70_database_path,
+                use_small_bfd=use_small_bfd,
+                no_cpus=args.cpus,
+            )
+            t = time.perf_counter()
+            alignment_data_workflow_runner.run(fasta_path, alignment_dir=local_alignment_dir)
+            print(f"Alignment data workflow time: {time.perf_counter() - t}")
+        else:
+            alignment_runner = data_pipeline.AlignmentRunner(
+                jackhmmer_binary_path=args.jackhmmer_binary_path,
+                hhblits_binary_path=args.hhblits_binary_path,
+                hhsearch_binary_path=args.hhsearch_binary_path,
+                uniref90_database_path=args.uniref90_database_path,
+                mgnify_database_path=args.mgnify_database_path,
+                bfd_database_path=args.bfd_database_path,
+                uniclust30_database_path=args.uniclust30_database_path,
+                pdb70_database_path=args.pdb70_database_path,
+                use_small_bfd=use_small_bfd,
+                no_cpus=args.cpus,
+            )
+            alignment_runner.run(fasta_path, local_alignment_dir)
+
+    feature_dict = data_processor.process_fasta(fasta_path=fasta_path,
+                                                alignment_dir=local_alignment_dir)
+
+    # Remove temporary FASTA file
+    os.remove(fasta_path)
+
+    processed_feature_dict = feature_processor.process_features(
+        feature_dict,
+        mode='predict',
+    )
+
+    batch = processed_feature_dict
+
+    manager = mp.Manager()
+    result_q = manager.Queue()
+    torch.multiprocessing.spawn(inference_model,
+                                nprocs=args.hpus,
+                                args=(args.hpus, result_q, batch, args))
+
+    out = result_q.get()
+
+    # Toss out the recycling dimensions --- we don't need them anymore
+    batch = tensor_tree_map(lambda x: np.array(x[..., -1].cpu()), batch)
+
+    plddt = out["plddt"]
+    mean_plddt = np.mean(plddt)
+
+    plddt_b_factors = np.repeat(plddt[..., None], residue_constants.atom_type_num, axis=-1)
+
+    unrelaxed_protein = protein.from_prediction(features=batch,
+                                                result=out,
+                                                b_factors=plddt_b_factors)
+
+    # Save the unrelaxed PDB.
+    unrelaxed_output_path = os.path.join(args.output_dir, f'{tag}_{args.model_name}_unrelaxed.pdb')
+    with open(unrelaxed_output_path, 'w') as f:
+        f.write(protein.to_pdb(unrelaxed_protein))
+
+    amber_relaxer = relax.AmberRelaxation(
+        use_gpu=False,
+        **config.relax,
+    )
+
+    # Relax the prediction.
+    t = time.perf_counter()
+    relaxed_pdb_str, _, _ = amber_relaxer.process(prot=unrelaxed_protein)
+    print(f"Relaxation time: {time.perf_counter() - t}")
+
+    # Save the relaxed PDB.
+    relaxed_output_path = os.path.join(args.output_dir, f'{tag}_{args.model_name}_relaxed.pdb')
+    with open(relaxed_output_path, 'w') as f:
+        f.write(relaxed_pdb_str)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "fasta_path",
+        type=str,
+    )
+    parser.add_argument(
+        "template_mmcif_dir",
+        type=str,
+    )
+    parser.add_argument("--use_precomputed_alignments",
+                        type=str,
+                        default=None,
+                        help="""Path to alignment directory. If provided, alignment computation 
+                is skipped and database path arguments are ignored.""")
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default=os.getcwd(),
+        help="""Name of the directory in which to output the prediction""",
+    )
+    parser.add_argument("--model_name",
+                        type=str,
+                        default="model_1",
+                        help="""Name of a model config. Choose one of model_{1-5} or 
+             model_{1-5}_ptm or model_{1-5}_multimer, as defined on the AlphaFold GitHub.""")
+    parser.add_argument("--param_path",
+                        type=str,
+                        default=None,
+                        help="""Path to model parameters. If None, parameters are selected
+             automatically according to the model name from 
+             ./data/params""")
+    parser.add_argument("--cpus",
+                        type=int,
+                        default=12,
+                        help="""Number of CPUs with which to run alignment tools""")
+    parser.add_argument("--hpus",
+                        type=int,
+                        default=1,
+                        help="""Number of GPUs with which to run inference""")
+    parser.add_argument('--preset',
+                        type=str,
+                        default='full_dbs',
+                        choices=('reduced_dbs', 'full_dbs'))
+    parser.add_argument('--data_random_seed', type=str, default=None)
+    parser.add_argument(
+        "--model_preset",
+        type=str,
+        default="monomer",
+        choices=["monomer", "multimer"],
+        help="Choose preset model configuration - the monomer model, the monomer model with "
+        "extra ensembling, monomer model with pTM head, or multimer model",
+    )
+    add_data_args(parser)
+    args = parser.parse_args()
+
+    if (args.param_path is None):
+        args.param_path = os.path.join("data", "params", "params_" + args.model_name + ".npz")
+
+    main(args)

habana/inference.sh

+# add '--gpus [N]' to use N gpus for inference
+# add '--enable_workflow' to use parallel workflow for data processing
+# add '--use_precomputed_alignments [path_to_alignments]' to use precomputed msa
+# add '--chunk_size [N]' to use chunk to reduce peak memory
+# add '--inplace' to use inplace to save memory
+
+python habana/inference.py target.fasta data/pdb_mmcif/mmcif_files \
+    --output_dir ./ \
+    --uniref90_database_path data/uniref90/uniref90.fasta \
+    --mgnify_database_path data/mgnify/mgy_clusters_2018_12.fa \
+    --pdb70_database_path data/pdb70/pdb70 \
+    --uniclust30_database_path data/uniclust30/uniclust30_2018_08/uniclust30_2018_08 \
+    --bfd_database_path data/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
+    --jackhmmer_binary_path `which jackhmmer` \
+    --hhblits_binary_path `which hhblits` \
+    --hhsearch_binary_path `which hhsearch` \
+    --kalign_binary_path `which kalign`

habana/inference_test.py

+import pickle
+import time
+
+import habana_frameworks.torch.core as htcore
+import torch
+
+import fastfold.habana as habana
+from fastfold.config import model_config
+from fastfold.habana.distributed import init_dist
+from fastfold.habana.fastnn.ops import set_chunk_size
+from fastfold.habana.inject_habana import inject_habana
+from fastfold.model.hub import AlphaFold
+
+def main():
+    habana.enable_habana()
+
+    init_dist()
+    batch = pickle.load(open('./test_batch.pkl', 'rb'))
+
+    model_name = "model_1"
+    device = torch.device("hpu")
+
+    config = model_config(model_name)
+    config.globals.inplace = False
+    config.globals.chunk_size = 512
+    # habana.enable_hmp()
+    model = AlphaFold(config)
+    model = inject_habana(model)
+    model = model.eval()
+    model = model.to(device=device)
+
+    if config.globals.chunk_size is not None:
+        set_chunk_size(model.globals.chunk_size + 1)
+
+    if habana.is_hmp():
+        from habana_frameworks.torch.hpex import hmp
+        hmp.convert(opt_level='O1',
+                    bf16_file_path='./habana/ops_bf16.txt',
+                    fp32_file_path='./habana/ops_fp32.txt',
+                    isVerbose=False)
+        print("========= AMP ENABLED!!")
+
+    with torch.no_grad():
+        batch = {k: torch.as_tensor(v).to(device=device) for k, v in batch.items()}
+
+        for _ in range(5):
+            t = time.perf_counter()
+            out = model(batch)
+            htcore.mark_step()
+            htcore.hpu.default_stream().synchronize()
+            print(f"Inference time: {time.perf_counter() - t}")
+
+
+if __name__ == '__main__':
+    main()

habana/ops_bf16.txt

+addmm
+conv2d
+max_pool2d
+sum
+relu
+mm
+bmm
+mv
+linear
+t
+mul
+sub
+add
+truediv
+layer_norm

habana/ops_fp32.txt

+cross_entropy
+log_softmax
+nll_loss
+softmax

habana/train.py

+import argparse
+import logging
+import random
+
+import numpy as np
+import torch
+from torch.nn.parallel import DistributedDataParallel as DDP
+from tqdm import tqdm
+
+import fastfold.habana as habana
+from fastfold.config import model_config
+from fastfold.data.data_modules import SetupTrainDataset, TrainDataLoader
+from fastfold.habana.distributed import init_dist
+from fastfold.habana.inject_habana import inject_habana
+from fastfold.model.hub import AlphaFold, AlphaFoldLoss, AlphaFoldLRScheduler
+from fastfold.utils.tensor_utils import tensor_tree_map
+
+import habana_frameworks.torch.core as htcore
+from habana_frameworks.torch.hpex import hmp
+
+logging.disable(logging.WARNING)
+
+torch.multiprocessing.set_sharing_strategy('file_system')
+
+from habana.hpuhelper import *
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--from_torch', default=False, action='store_true')
+    parser.add_argument("--template_mmcif_dir",
+                        type=str,
+                        help="Directory containing mmCIF files to search for templates")
+    parser.add_argument("--max_template_date",
+                        type=str,
+                        help='''Cutoff for all templates. In training mode, templates are also 
+                filtered by the release date of the target''')
+    parser.add_argument("--train_data_dir",
+                        type=str,
+                        help="Directory containing training mmCIF files")
+    parser.add_argument("--train_alignment_dir",
+                        type=str,
+                        help="Directory containing precomputed training alignments")
+    parser.add_argument(
+        "--train_chain_data_cache_path",
+        type=str,
+        default=None,
+    )
+    parser.add_argument("--distillation_data_dir",
+                        type=str,
+                        default=None,
+                        help="Directory containing training PDB files")
+    parser.add_argument("--distillation_alignment_dir",
+                        type=str,
+                        default=None,
+                        help="Directory containing precomputed distillation alignments")
+    parser.add_argument(
+        "--distillation_chain_data_cache_path",
+        type=str,
+        default=None,
+    )
+    parser.add_argument("--val_data_dir",
+                        type=str,
+                        default=None,
+                        help="Directory containing validation mmCIF files")
+    parser.add_argument("--val_alignment_dir",
+                        type=str,
+                        default=None,
+                        help="Directory containing precomputed validation alignments")
+    parser.add_argument("--kalign_binary_path",
+                        type=str,
+                        default='/usr/bin/kalign',
+                        help="Path to the kalign binary")
+    parser.add_argument("--train_filter_path",
+                        type=str,
+                        default=None,
+                        help='''Optional path to a text file containing names of training
+                examples to include, one per line. Used to filter the training 
+                set''')
+    parser.add_argument("--distillation_filter_path",
+                        type=str,
+                        default=None,
+                        help="""See --train_filter_path""")
+    parser.add_argument("--obsolete_pdbs_file_path",
+                        type=str,
+                        default=None,
+                        help="""Path to obsolete.dat file containing list of obsolete PDBs and 
+             their replacements.""")
+    parser.add_argument("--template_release_dates_cache_path",
+                        type=str,
+                        default=None,
+                        help="""Output of scripts/generate_mmcif_cache.py run on template mmCIF
+                files.""")
+    parser.add_argument("--train_epoch_len",
+                        type=int,
+                        default=10000,
+                        help=("The virtual length of each training epoch. Stochastic filtering "
+                              "of training data means that training datasets have no "
+                              "well-defined length. This virtual length affects frequency of "
+                              "validation & checkpointing (by default, one of each per epoch)."))
+    parser.add_argument("--_alignment_index_path",
+                        type=str,
+                        default=None,
+                        help="Training alignment index. See the README for instructions.")
+    parser.add_argument("--config_preset",
+                        type=str,
+                        default="initial_training",
+                        help=('Config setting. Choose e.g. "initial_training", "finetuning", '
+                              '"model_1", etc. By default, the actual values in the config are '
+                              'used.'))
+    parser.add_argument(
+        "--_distillation_structure_index_path",
+        type=str,
+        default=None,
+    )
+    parser.add_argument("--distillation_alignment_index_path",
+                        type=str,
+                        default=None,
+                        help="Distillation alignment index. See the README for instructions.")
+    parser.add_argument("--seed", type=int, default=42, help="Random seed")
+
+    # habana arguments
+    parser.add_argument("--hmp",
+                        action='store_true',
+                        default=False,
+                        help="Whether to use habana mixed precision")
+    parser.add_argument("--hmp-bf16",
+                        type=str,
+                        default="./habana/ops_bf16.txt",
+                        help="Path to bf16 ops list in hmp O1 mode")
+    parser.add_argument("--hmp-fp32",
+                        type=str,
+                        default="./habana/ops_fp32.txt",
+                        help="Path to fp32 ops list in hmp O1 mode")
+    parser.add_argument("--hmp-opt-level",
+                        type=str,
+                        default='O1',
+                        help="Choose optimization level for hmp")
+    parser.add_argument("--hmp-verbose",
+                        action='store_true',
+                        default=False,
+                        help='Enable verbose mode for hmp')
+
+    args = parser.parse_args()
+
+    habana.enable_habana()
+
+    init_dist()
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+
+    config = model_config(args.config_preset, train=True)
+    config.globals.inplace = False
+    model = AlphaFold(config)
+    model = inject_habana(model)
+
+    model = model.to(device="hpu")
+    model = DDP(model)
+
+    train_dataset, test_dataset = SetupTrainDataset(
+        config=config.data,
+        template_mmcif_dir=args.template_mmcif_dir,
+        max_template_date=args.max_template_date,
+        train_data_dir=args.train_data_dir,
+        train_alignment_dir=args.train_alignment_dir,
+        train_chain_data_cache_path=args.train_chain_data_cache_path,
+        distillation_data_dir=args.distillation_data_dir,
+        distillation_alignment_dir=args.distillation_alignment_dir,
+        distillation_chain_data_cache_path=args.distillation_chain_data_cache_path,
+        val_data_dir=args.val_data_dir,
+        val_alignment_dir=args.val_alignment_dir,
+        kalign_binary_path=args.kalign_binary_path,
+        # train_mapping_path=args.train_mapping_path,
+        # distillation_mapping_path=args.distillation_mapping_path,
+        obsolete_pdbs_file_path=args.obsolete_pdbs_file_path,
+        template_release_dates_cache_path=args.template_release_dates_cache_path,
+        train_epoch_len=args.train_epoch_len,
+        _alignment_index_path=args._alignment_index_path,
+    )
+
+    train_dataloader, test_dataloader = TrainDataLoader(
+        config=config.data,
+        train_dataset=train_dataset,
+        test_dataset=test_dataset,
+        batch_seed=args.seed,
+    )
+
+    criterion = AlphaFoldLoss(config.loss)
+
+    # optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, eps=1e-8)
+    from habana_frameworks.torch.hpex.optimizers import FusedAdamW
+    optimizer = FusedAdamW(model.parameters(), lr=1e-3, eps=1e-8)
+
+    lr_scheduler = AlphaFoldLRScheduler(optimizer)
+
+    if args.hmp:
+        hmp.convert(opt_level='O1',
+                    bf16_file_path=args.hmp_bf16,
+                    fp32_file_path=args.hmp_fp32,
+                    isVerbose=args.hmp_verbose)
+        print("========= HMP ENABLED!!")
+
+    for epoch in range(200):
+        model.train()
+        train_dataloader = tqdm(train_dataloader)
+        for batch in train_dataloader:
+            perf = hpu_perf("train step")
+            batch = {k: torch.as_tensor(v).to(device="hpu") for k, v in batch.items()}
+            optimizer.zero_grad()
+            output = model(batch)
+            perf.checknow("forward")
+            
+            batch = tensor_tree_map(lambda t: t[..., -1], batch)
+            loss, loss_breakdown = criterion(output, batch, _return_breakdown=True)
+            perf.checknow("loss")
+
+            loss.backward()
+            train_dataloader.set_postfix(loss=float(loss))
+            perf.checknow("backward")
+
+            with hmp.disable_casts():
+                optimizer.step()
+            perf.checknow("optimizer")
+
+        lr_scheduler.step()
+
+        if test_dataloader is not None:
+            model.eval()
+            train_dataloader = tqdm(train_dataloader)
+            for batch in test_dataloader:
+                batch = {k: torch.as_tensor(v).to(device="hpu") for k, v in batch.items()}
+                with torch.no_grad():
+                    output = model(batch)
+                    batch = tensor_tree_map(lambda t: t[..., -1], batch)
+                    _, loss_breakdown = criterion(output, batch, _return_breakdown=True)
+
+                    htcore.mark_step()
+                    train_dataloader.set_postfix(loss=float(loss))
+
+
+if __name__ == "__main__":
+    main()

habana/train.sh

+DATA_DIR=/mnt/usb/training-demo
+
+hpus_per_node=1
+
+max_template_date=2021-10-10
+
+train_data_dir=${DATA_DIR}/mmcif_dir  # specify the dir contains *.cif or *.pdb
+train_alignment_dir=${DATA_DIR}/alignment_dir  # a dir to save template and features.pkl of training sequence
+mkdir -p ${train_alignment_dir}
+
+# val_data_dir=${PROJECT_DIR}/dataset/val_pdb
+# val_alignment_dir=${PROJECT_DIR}/dataset/alignment_val_pdb  # a dir to save template and features.pkl of vld sequence
+
+template_mmcif_dir=${DATA_DIR}/data/pdb_mmcif/mmcif_files
+template_release_dates_cache_path=${DATA_DIR}/mmcif_cache.json  # a cache used to pre-filter templates
+train_chain_data_cache_path=${DATA_DIR}/chain_data_cache.json  # a separate chain-level cache with data used for training-time data filtering
+
+train_epoch_len=10000  # virtual length of each training epoch, which affects frequency of validation & checkpointing
+
+mpirun --allow-run-as-root --bind-to none -np ${hpus_per_node} python habana/train.py \
+    --from_torch \
+    --template_mmcif_dir=${template_mmcif_dir} \
+    --max_template_date=${max_template_date} \
+    --train_data_dir=${train_data_dir} \
+    --train_alignment_dir=${train_alignment_dir} \
+    --train_chain_data_cache_path=${train_chain_data_cache_path} \
+    --template_release_dates_cache_path=${template_release_dates_cache_path} \
+    --train_epoch_len=${train_epoch_len} \

setup.py

@@ -46,23 +46,24 @@ def append_nvcc_threads(nvcc_extra_args):
 
 
 if not torch.cuda.is_available():
-    # https://github.com/NVIDIA/apex/issues/486
-    # Extension builds after https://github.com/pytorch/pytorch/pull/23408 attempt to query torch.cuda.get_device_capability(),
-    # which will fail if you are compiling in an environment without visible GPUs (e.g. during an nvidia-docker build command).
-    print(
-        '\nWarning: Torch did not find available GPUs on this system.\n',
-        'If your intention is to cross-compile, this is not an error.\n'
-        'By default, FastFold will cross-compile for Pascal (compute capabilities 6.0, 6.1, 6.2),\n'
-        'Volta (compute capability 7.0), Turing (compute capability 7.5),\n'
-        'and, if the CUDA version is >= 11.0, Ampere (compute capability 8.0).\n'
-        'If you wish to cross-compile for a single specific architecture,\n'
-        'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n')
-    if os.environ.get("TORCH_CUDA_ARCH_LIST", None) is None:
-        _, bare_metal_major, _ = get_cuda_bare_metal_version(CUDA_HOME)
-        if int(bare_metal_major) == 11:
-            os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5;8.0"
-        else:
-            os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5"
+    print("======== NOTICE: torch.cuda.is_available == False")
+#     # https://github.com/NVIDIA/apex/issues/486
+#     # Extension builds after https://github.com/pytorch/pytorch/pull/23408 attempt to query torch.cuda.get_device_capability(),
+#     # which will fail if you are compiling in an environment without visible GPUs (e.g. during an nvidia-docker build command).
+#     print(
+#         '\nWarning: Torch did not find available GPUs on this system.\n',
+#         'If your intention is to cross-compile, this is not an error.\n'
+#         'By default, FastFold will cross-compile for Pascal (compute capabilities 6.0, 6.1, 6.2),\n'
+#         'Volta (compute capability 7.0), Turing (compute capability 7.5),\n'
+#         'and, if the CUDA version is >= 11.0, Ampere (compute capability 8.0).\n'
+#         'If you wish to cross-compile for a single specific architecture,\n'
+#         'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n')
+#     if os.environ.get("TORCH_CUDA_ARCH_LIST", None) is None:
+#         _, bare_metal_major, _ = get_cuda_bare_metal_version(CUDA_HOME)
+#         if int(bare_metal_major) == 11:
+#             os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5;8.0"
+#         else:
+#             os.environ["TORCH_CUDA_ARCH_LIST"] = "6.0;6.1;6.2;7.0;7.5"
 
 print("\n\ntorch.__version__  = {}\n\n".format(torch.__version__))
 TORCH_MAJOR = int(torch.__version__.split('.')[0])
@@ -82,11 +83,7 @@ ext_modules = []
 # https://github.com/pytorch/pytorch/commit/eb7b39e02f7d75c26d8a795ea8c7fd911334da7e#diff-4632522f237f1e4e728cb824300403ac
 version_dependent_macros = ['-DVERSION_GE_1_1', '-DVERSION_GE_1_3', '-DVERSION_GE_1_5']
 
-if CUDA_HOME is None:
-    raise RuntimeError(
-        "Are you sure your environment has nvcc available?  If you're installing within a container from https://hub.docker.com/r/pytorch/pytorch, only images whose names contain 'devel' will provide nvcc."
-    )
-else:
+if CUDA_HOME:
     # check_cuda_torch_binary_vs_bare_metal(CUDA_HOME)
 
     def cuda_ext_helper(name, sources, extra_cuda_flags):
@@ -126,6 +123,8 @@ else:
     ext_modules.append(
         cuda_ext_helper('fastfold_softmax_cuda', ['softmax_cuda.cpp', 'softmax_cuda_kernel.cu'],
                         extra_cuda_flags + cc_flag))
+else:
+    print("======== NOTICE: install without cuda kernel")
 
 setup(
     name='fastfold',