Sketch out conj method by dallonasnes · Pull Request #46 · danlkv/QTensor

dallonasnes · 2023-10-09T23:01:28Z

Beginning of conj method

dallonasnes · 2023-10-09T23:01:44Z

qtensor/Simulate.py

+                if shared_indices.intersection(set(pair_node.get_all_dangling())):
+                    edge = tn.connect(node, pair_node)
+
+        # TODO: TNAdapter should support tensornetwork.Node


@danlkv this is my TODO for next week

dallonasnes · 2023-10-21T10:37:27Z

scratchpad/tn_api/tn.py

+from qtree import np_framework

 class Array(np.ndarray):
-    shape: tuple[int]


removed all instances of tuple[... because was getting error that types can't have [

dallonasnes · 2023-10-21T10:38:22Z

scratchpad/tn_api/tn.py

    def slice(self, slice_dict: dict) -> 'TensorNetwork':
-        ...
+        tn = self.copy()
+        sliced_buckets = np_framework.get_sliced_np_buckets(self.buckets, self.data_dict, slice_dict)


i imagine data_dict needs to be populated for this to be meaningful logic. is that something the callers should pass in? @danlkv

danlkv · 2023-10-21T16:19:45Z

The tensor class shouldn't contain buckets, it would be duplicating information, since we already have tensors property. The slice method should take the dict and for every index find those tensors that have the index. Then slice those tensors using the values in dict, and replace the original ones. Note also that the tensors are numpy arrays in this class

…

On Sat, Oct 21, 2023, 5:38 AM Dallon Asnes ***@***.***> wrote: ***@***.**** commented on this pull request. ------------------------------ In scratchpad/tn_api/tn.py <#46 (comment)>: > # slice not inplace def slice(self, slice_dict: dict) -> 'TensorNetwork': - ... + tn = self.copy() + sliced_buckets = np_framework.get_sliced_np_buckets(self.buckets, self.data_dict, slice_dict) i imagine data_dict needs to be populated for this to be meaningful logic. is that something the callers should pass in? @danlkv <https://github.com/danlkv> — Reply to this email directly, view it on GitHub <#46 (review)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ADET2QO3G5X3AMHZEE5BB3LYAOQ2RAVCNFSM6AAAAAA5ZOEYEGVHI2DSMVQWIX3LMV43YUDVNRWFEZLROVSXG5CSMV3GSZLXHMYTMOJRGIZTEOJYGI> . You are receiving this because you were mentioned.Message ID: ***@***.***>

dallonasnes · 2023-10-22T07:53:54Z

scratchpad/tn_api/tn.py

        tn = self.copy()
-        sliced_buckets = np_framework.get_sliced_np_buckets(self.buckets, self.data_dict, slice_dict)
-        tn.buckets = sliced_buckets
+        sliced_tns = []


@danlkv i think this implementation is better, wdyt?

Yes, this is much better. However, we need a way to refer to tensor indices globally. For example, what would happen if using this code we sliced a tensor with 3 tensors of shape (2, 2) and slice dict {1: 0}? The answer is that all tensors will be sliced along the 2nd dimension, but we want a behavior that is global to all TN

In addition, you have to consider how the edges change as well.

@dallonasnes Here's a sketch of what can be a solution.
First, let me clear out something about the edges structure. Edges in the edge array represent delta-tensors, which make all indices that are connected to them the same. For example for expression T_{abc}\delta_{abd} B_{edg}C_{mp} the equivalent expression is T_{xxc } B_{exg} C_{mp}. Note that you can have a one-to-one correspondence between each delta and an index (in the example index x).

Let's assume that all indices in our TN are represented by a delta tensor and its corresponding tuple in the edges attribute (This may seem excessive, but we can find overcome the overhead later). Now, a slice dict may refer to the element in the edges attribute by using just index of the edge.

So the slice dict is {index in edge dict: index value}

The algorithm would be
1 For each index, value pair of slice dict

Pop the edge from edges attr using the index

Get all tensors that are indexed by this edge (in the example above that would be T and B). Use Port.tensor_ix for that.

Using the information in ports in edges, construct the local (wrt each tensor) slicing dict for each tensor

Slice and update the tensor.

Note that after slicing the edge disappears.

dallonasnes · 2023-10-30T16:16:36Z

scratchpad/tn_api/tn.py

+        self._indices = {}
+
+    # slice not inplace
+    def slice(self, slice_dict: dict) -> 'TensorNetwork':


@danlkv thanks for the previous comment, i believed i've addressed that in this update

scratchpad/tn_api/tn.py

dallonasnes · 2023-10-30T22:42:08Z

scratchpad/tn_api/tn.py

+
+    # contract to produce a new tensor
+    def contract(self, contraction_info: ContractionInfo) -> np.ndarray:
+        raise NotImplementedError()


contraction_info here is indices i'd want in the output
einsum

test case is contracted A = contraction of sliceA[0] and sliceA[1]. this is true for any index so can select it at random

dallonasnes · 2023-12-04T12:50:12Z

scratchpad/tn_api/tn.py

+                sliced_tensor = tn._tensors[current_tensor_ref][tuple(slice_bounds)]
+                tn._tensors[current_tensor_ref] = sliced_tensor
+
+                # TODO: this is just a guess - but i am adding the ports from the popped edge back to the list of slices


@danlkv where we left off last time is that calling einsum is failing on a sliced tn. it's likely because slicing the tn removes all edges - i don't think that behavior is intended, so my proposal for a fix is that in this section of logic (starting at line 76) when we pop an edge out, at some point we need to add it back.

i've added some pdb loops so that when we err out, we can repro it in the same run

here's an example error output

aa,ba->ab [(4, 4, 2), (4, 4, 2)] ((Port(tensor_ref=0, ix=0), Port(tensor_ref=0, ix=1), Port(tensor_ref=1, ix=1)), (Port(tensor_ref=1, ix=0),)) 2 operand has more dimensions than subscripts given in einstein sum, but no '...' ellipsis provided to broadcast the extra dimensions. [1] > /app/scratchpad/tn_api/tn.py(172)contract() -> keep_going = True (Pdb++) c Traceback (most recent call last): File "tn_api/tn.py", line 168, in contract return np.einsum(einsum_expr, *self._tensors) File "<__array_function__ internals>", line 200, in einsum File "/usr/local/lib/python3.8/dist-packages/numpy/core/einsumfunc.py", line 1371, in einsum return c_einsum(*operands, **kwargs) ValueError: operand has more dimensions than subscripts given in einstein sum, but no '...' ellipsis provided to broadcast the extra dimensions.

Sketch out conj method

eeba989

dallonasnes requested a review from danlkv October 9, 2023 23:01

dallonasnes commented Oct 9, 2023

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danlkv and others added 2 commits October 18, 2023 00:03

add draft of tn api

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Use np sliced buckets method from qtree

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dallonasnes commented Oct 21, 2023

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Update

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dallonasnes force-pushed the conj-api branch from 55dda4f to b0645a2 Compare October 22, 2023 07:53

dallonasnes commented Oct 22, 2023

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dallonasnes commented Oct 30, 2023

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dallonasnes commented Oct 30, 2023

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dallonasnes commented Oct 30, 2023

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dallonasnes added 10 commits November 6, 2023 10:43

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Fix copy so that it is not in place

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dallonasnes commented Dec 4, 2023

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dallonasnes commented Oct 9, 2023

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danlkv commented Oct 21, 2023 via email

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